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Ms-11 Strategic Management Solved Assignment 1424

1. Introduction

The ubiquitous presence of devices combined with intelligent sensing [1,2], low-power operation [3,4], and communication ability [5,6] enable the rapid expansion of the Internet of Things (IoT). Additionally, the IoT delivers a reliable, efficient, and greener energy transmission to better support services in sustainable smart cities. The challenges of IoT involve energy consumption, bandwidth requirement dynamic communication environment, and etc. Regarding energy consumption, the most severe challenge is that nodes are significantly influenced by limited battery capacity and it is difficult to replace or recharge their batteries in run-time. Thus, effective power management is very important in IoT [7], e.g., so that it can monitor the components of energy consumption and dynamically switch the state of each node accordingly.

Currently, most algorithms of power management focus on constrained optimization problem to minimize power consumption under performance constraints [8,9]. However, in the distributed network, each node is difficult to autonomously save energy without any guideline. Moreover, the power consumption in anti-interference and data retransmission will increase sharply with the expansion of the devices. Multi-channel communication, which allows simultaneously transmissions along multiple non-overlap channels, is a promising approach to reduce interference. Nevertheless, there are two research challenges in multi-channel communications that have not been addressed by the existing research need to be explored: (i) Most channel allocation processes ignore the problem of energy consumption varies on different channels; and (ii) Less attention is laid in the channel quality.

To address these two challenges, we design a solution that jointly considers channel allocation and time slot optimization. In our solution, each node will learn and make decision based on its own local information in a distributed manner, without requiring information exchanges and time synchronizations between neighboring nodes. The energy saving is a joint optimization in both frequency domain and time domain, i.e., via channel allocation and time slot adaptation. The benefits for this are two-fold. First, in frequency domain, we employ channel allocation to save energy. As the channel allocation plays an important role in two aspects: (i) it can enable each node simultaneously transmits along different channels to alleviate interference and reduce wasteful energy in anti-interference and data retransmission; (ii) it can allocate an appropriate channel with lower energy consumption to node. Second, in time domain, we employ the time slot optimization to save energy. In our proposed scheme, the sleeping time and spectrum sensing time are adaptive and always keep the optimal values. This optimized adaptation has two significant advantages: (i) the optimal sensing time yields the highest throughput. On one hand, too short sensing time cannot successfully detect the channel state, and thus transmission collisions diminish the throughput. On the other hand, too long sensing time will consume more energy and thus left less energy for data transmission, which also diminish the throughput; (ii) the optimal sleeping time prolongs the lifetime of nodes. If the sleeping time is too short when deals with the increasing traffic arrival rate, it will drain the energy much faster and thus cannot guarantee the lifetime of nodes. On the contrary, if the sleeping time is too long, it cannot guarantee the throughput. Therefore, the optimal solution can not only control the power consumption but also maximum the normalized throughput.

The paper is organized as follows: Section 2 gives an overview of related work about energy management in IoT. Section 3 summarizes the distributed channel allocation and time slot optimization algorithms, and presents the transmission structure of each node. Section 4 proposes a channel ranking algorithm so that each node can rank its available channels based on the channel condition. Section 5 proposes a distributed channel allocation algorithm that each node can choose proper channel based on the channel ranking and its own experience. Section 6 analyzes the data transmission rate and derives the optimal solution. Section 7 presents the results of our experiments for each of the aforementioned cases. Section 8 concludes this paper.

2. Related Work

Power efficiency plays an extremely important role in the rapid development of IoT, and the challenge of the power efficiency is how to guarantee the network performance while minimizing the energy consumption. In the literature, various power management algorithms have been proposed, which can be broadly classified into two categories: power control and time slot control.

Dynamic power management (DPM) [10] is proposed to selectively power off the idle part to save energy, but the disadvantage of this approach is that it requires complete knowledge of past and future workloads to fulfill the target. The power control algorithm (PCA) in [11] controls the transmission power consumption according to different channel conditions. PCA can improve the overall energy efficiency, but the coordination between nodes is done implicitly, and thus a part of energy is consumed by beacon exchange. As for time slot control, energy consumption in sleeping period is much lower than data transmission period [12], and this implies it is worthwhile for nodes to make certain trade-off to balance the time between sleeping period and transmission period. Studies [13,14] propose that nodes can choose to switch into sleeping mode when there is no data to be received or transmitted. In [15], the algorithm balances the energy consumption and end-to-end delay by appropriately scheduling the sleeping time. Another important time control is about spectrum sensing period. Insufficient spectrum sensing period will lead to imperfect channel state information, while excessive spectrum sensing period will decrease the time for data transmission [16]. Both the sleeping period and spectrum sensing period impact the network performance and thus need to be jointly optimized.

The aforementioned algorithms can be applied to save the energy consumption effectively, but it still cannot radically deal with energy consumption if the interference is caused by the expansion of the network. Therefore, multi-channel communication is introduced to reduce the network interference [17]. In multi-channel communication, the channel allocation plays a crucial role, which can be achieved via two methods: cooperative method and non-cooperative method. Cooperative method can learn from the exchange of information among nodes, while non-cooperative method can learn by nodes’ own past experience [18]. In [19], a channel assignment with power constraint is proposed to maximize the total throughput. However, this method needs to exchange control signals and each node cannot totally decide its behavior in a distributed manner. The information exchange has to occupy certain time slots and energy so that the node’s life is shortened. Therefore, nodes are better to decide their behaviors by themselves without too much information exchange for negotiation. Another important problem for channel allocation is that most researches are based on the assumption that all channels are identical. In practice, in multi-cell OFDM systems, the sub-channels will be reused by nodes, which may cause interference to each other, moreover, different sub-channels may suffer from different noises and have various bandwidth, resulting in different transmission energy consumption. Therefore, study [11] adopts a closed-loop power-control (CLPC) algorithm, which adjusts the transmission power according to different channel conditions. Moreover, authors in [20] propose a channel allocation scheme considering the signal to noise ratio (SNR) of each channel. And then more research is conducted considering the effect of the channel width [21,22]. Different channel quality leads to different transmission power [11]; therefore, it is critical to allocate the high-quality channel to the node with lower residual energy to balance the node lifetime.

Most existing contributions on energy saving consider either only the impact of power management or only the impact of channel allocation. This paper aims to improve energy efficiency from both channel selection and time slot optimization. We first propose a distributed channel ranking algorithm so that each node can be allocated with ideal channel (e.g., the channel of higher rank in the channel ranking), while considering different properties of channels, including bandwidth, signal-to-interference-plus-noise ratio (SINR), coherent bandwidth, coherent time and channel energy consumption. We also enable each node to learn from history knowledge for better performance on convergence and scalability. After the channel has been determined, we jointly optimize the sleeping period and spectrum sensing time to maximize the data transmission rate while guaranteeing that the lifetime of nodes can reach the expected survival time. Specifically, the main contributions of our paper are summarized as follows:
  • We propose a novel solution that considers the complementary relations between channel allocation and time slot design for both energy saving and interference alleviating.

  • We present a channel allocation algorithm that takes channel conditions into consideration and involves learning ability. It can not only allocate the most ideal channel to each node, but also enable the channel allocation achieves fast collision-free convergence.

  • Through optimal time slot adaptation, our solution can maximize normalized throughput while satisfying power constraint and guaranteeing the lifetime of the nodes.

  • Our solution is easy to implement. It requires no central coordination, global information, or information exchange/time synchronization among nodes. Each node can operate solely based on its own local information in a distributed manner.

3. System Model

This paper focuses on data-gathering/data-collection applications of IoT, where the many-to-one/converge-cast communication model is applicable as shown in Figure 1. In this network, spatially distributed nodes monitor various physical and environmental factors, and deliver the acquired information to the control center. The control center is responsible for managing the whole collection. The framework adopts Orthogonal Frequency Division Multiplexing (OFDM) in physical layer. The spectrum band of 5 GHz is slipped into channels. We consider the frequency-selective Rayleigh fading channel models. We assume the network design using short range radio standards that can be used to develop a wide area networks to support large number of IoT devices for various applications in a city environment [23]. The results of this paper are also transferable to lower frequencies, e.g., Low Power Wide Area Network (LPWAN) technologies as well. As the paper focuses on optimization design for medium access control (MAC) protocol in high-density IoT network, it aims to improve energy efficiency through both channel allocation and time slot optimization.

We adopt the channel hopping technique to alleviate interference. The channel hopping technique has been embraced by a number of technologies and standards. One of the most notable examples, the IEEE 802.15.4e Task Group [24] adopts the time slotted channel hopping (TSCH) technique to enhance the existing IEEE 802.15.4 standard. In our solution, each node is free to switch among these m channels, but can only use one channel to transmit at a time. When two nodes are within the interference range, collision will occur if they transmit on the same channel simultaneously.

In our system model, we consider computation offloading as an effective approach to solve the energy saving problem. In traditional IoT network, each central station should be able to support a massive number of IoT devices, which bring high computation burden to the central station in dense networks. Therefore, we adopt computation offloading technique to enable distributed nodes afford parts of the computation from the control center to alleviate its computation burden [25]. We allow each node to make decision on (i) the transmit channel to obtain a collision-free channel allocation; (ii) the optimal time including sleeping time and spectrum sensing time.

The transmission structure of each node is described in Figure 2. We consider that time is divided into fixed length slotframes, and each slotframe is composed of 100 timeslots. Each slotframe has two processes: channel allocation and transmission. In the channel allocation process, the quality of channels need be firstly measured by Energy Detections (ED) [26], because the network conditions vary over time. ED can detect energy from variable-bandwidth channels. As the variable bandwidth will suffer different degree of noise power, thus a spectrum analyzer with different low pass filters is needed to measure the noise. Then, we update the available channel set (ACS) of each node based on the rank of channel condition. The node will dynamically hop between channels according to ACS until it switches on an empty channel, and then start to send HELLO packet periodically to dwell on this channel. In the transmission process, as the node has chosen the channel, it will adjust variable timeslots of sleeping and spectrum sensing based on the residual energy to ensure the transmission rate. If the selected channel is sensed idle, a transmission starts. Otherwise, the node has to continue to sleep. The node may go through successive sleeping and spectrum sensing before data transmission.

Furthermore, as the network does not need synchronization, the following scenario may occur as shown in Figure 3. Although node 1 and node 3 start channel allocation at different time slot, they will try to dwell on different channels to avoid interference, because their channel allocation periods overlap. However, node 2 starts channel allocation while the others two nodes (node 1 and node 3) finish the channel allocation. Since node 1 sleeps on CH1, node 2 considers CH1 as idle and thus dwells on CH1. Therefore, in order to avoid transmission collision, both node 1 and node 2 need to sense spectrum before transmission. If the channel is sensed busy, the node will continue to sleep until the channel is idle. Note that during the transmission period, nodes will not hop between channels, they will dwell on the allocated channel. When the next slotframe comes, they will hop again to achieve a collision-free channel allocation. For example, node 1 switches to an idle channel (e.g., CH3) to avoid interference with node 2 when the next channel allocation period arrives.

4. Channel Ranking Algorithm

The channel ranking algorithm aims to select the most desirable channel from all the available channels. Based on existing research [11,27,28,29], the important factors that affect channel conditions include bandwidth, Signal to Interference plus Noise Ratio (SINR), coherent bandwidth, coherent time and energy consumption. As the transmitter is capable to know which channel to hop and how many subcarriers can employ, thus the bandwidth of each channel is known and we assume the bandwidth is variable in 200 kHz–1 MHz. The other factors can be obtained via sending probe packets as proposed in [29].

  • Bandwidth is an important attribute that affect wireless transmission in terms of rate and range. Moreover, applications require different bandwidth. For example, status indicators for temperature will send a very small amount of data, while camera sensors that transmit a video stream will send a much greater amount of data [23].

  • SINR is used to give theoretical upper bounds on the channel capacity.

  • The coherence bandwidth is a statistical measurement of the range of frequencies. It can be reasonably assumed that the channel is flat if the coherence bandwidth is greater than the data signal bandwidth.

  • The coherence time is the time duration over which the channel impulse response is considered to be constant. Such channel variation is important in wireless communications systems, considering the Doppler effect.

  • The energy consumption on difficult channel mainly depends on the transmission power. Moreover, the transmission power varies according to different channel conditions based on the closed-loop power-control (CLPC) algorithm [11]. CLPC can dynamically degrade the transmission power in a flat channel to save energy and increase the transmission power to compensate for the fading channel.

Inspired by the Multiple Attribute Decision Making (MADM) algorithm [30], we propose a channel ranking algorithm that each node will rank its available channels based on the aforementioned five attributes. First of all, we need to identify the weight of each attribute through pairwise comparisons. And then we can rank channels via analyzing the closeness of each channel to the best channel condition.

4.1. Identify the Weight of Each Attribute

The weights of diversified attributes are determined by the following three steps.

(1) Construct the attribute comparison matrix

We first construct a matrix for the attribute comparison as follows,

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254BT-09 Bioinformatics & Intellectual Property RightsBSC-IIIBio Technology
255BT-09 Bioinformatics & Intellectual Property RightsBSC2L-3Bio Technology
256BT-09 Bioinformatics & Intellectual Property RightsBSC-IIIBio Technology
257BT-09 Bioinformatics & Intellectual Property RightsBScA(BT)Bio Technology
258BT-09 Bioinformatics & Intellectual Property RightsBSC3L-3Bio Technology
259BT-09 Bioinformatics & Intellectual Property RightsBSC2L-3Bio Technology
260BT-09 Bioinformatics & Intellectual Property RightsBSC2L-3Bio Technology
261BT-09 Bioinformatics & Intellectual Property RightsBSC3L-3Bio Technology
262BT-10 Nano BiotechnologyBSC3L-3Bio Technology
263BT-10 Nano BiotechnologyBSC2L-3Bio Technology
264BT-10 Nano BiotechnologyBSC2L-3Bio Technology
265BT-10 Nano BiotechnologyBSC3L-3Bio Technology
266BT-10 Nano BiotechnologyBScA(BT)Bio Technology
267BT-10 Nano BiotechnologyBSC-IIIBio Technology
268BT-10 Nano BiotechnologyBSC2L-3Bio Technology
269BT-10 Nano BiotechnologyBSC-IIIBio Technology
270BT-10 Nano BiotechnologyBSC-IIIBio Technology
271BT-10 Nano BiotechnologyBSC-IIIBio Technology
272BT-10 Nano BiotechnologyBSC-IIIBio Technology
273BT-11 Bioprocess TechnologyBSC-IIIBio Technology
274BT-11 Bioprocess TechnologyBSC-IIIBio Technology
275BT-11 Bioprocess TechnologyBSC-IIIBio Technology
276BT-11 Bioprocess TechnologyBSC-IIIBio Technology
277BT-11 Bioprocess TechnologyBSC2L-3Bio Technology
278BT-11 Bioprocess TechnologyBSC-IIIBio Technology
279BT-11 Bioprocess TechnologyBScA(BT)Bio Technology
280BT-11 Bioprocess TechnologyBSC3L-3Bio Technology
281BT-11 Bioprocess TechnologyBSC2L-3Bio Technology
282BT-11 Bioprocess TechnologyBSC2L-3Bio Technology
283BT-11 Bioprocess TechnologyBSC3L-3Bio Technology
284BT-12 Practical BiotechnologyBSC3L-3Bio Technology
285BT-12 Practical BiotechnologyBSC2L-3Bio Technology
286BT-12 Practical BiotechnologyBSC2L-3Bio Technology
287BT-12 Practical BiotechnologyBSC3L-3Bio Technology
288BT-12 Practical BiotechnologyBScA(BT)Bio Technology
289BT-12 Practical BiotechnologyBSC-IIIBio Technology
290BT-12 Practical BiotechnologyBSC2L-3Bio Technology
291BT-12 Practical BiotechnologyBSC-IIIBio Technology
292BT-12 Practical BiotechnologyBSC-IIIBio Technology
293BT-12 Practical BiotechnologyBSC-IIIBio Technology
294BT-12 Practical BiotechnologyBSC-IIIBio Technology
295CBIL-01 Banking LawsCBILNot Applicable
296CBIL-02 Insurance lawsCBILNot Applicable
297CBIL-03 Essay WritingCBILNot Applicable
298CCE-01 General Principles of Creative writingCCENot Applicable
299CCE-02 Elements of Creative WritingCCENot Applicable
300CCP-01 Crop ProductionCCPNot Applicable
301CCP-02 HorticultureCCPNot Applicable
302CCP-03 Plant Protection and ManagementCCPNot Applicable
303CFE-01 English in Specific SituationsCFENot Applicable
304CFE-03 English Usages and Language skillsCFENot Applicable
305CH-01 Inorganic ChemistryBSC-IChemistry
306CH-01 Inorganic ChemistryBSC-IChemistry
307CH-01 Inorganic ChemistryBSC-IChemistry
308CH-01 Inorganic ChemistryBSC-IChemistry
309CH-01 Inorganic ChemistryBScA(CH)Chemistry
310CH-02 Organic ChemistryBScA(CH)Chemistry
311CH-02 Organic ChemistryBSC-IChemistry
312CH-02 Organic ChemistryBSC-IChemistry
313CH-02 Organic ChemistryBSC-IChemistry
314CH-02 Organic ChemistryBSC-IChemistry
315CH-03 Physical ChemistryBSC-IChemistry
316CH-03 Physical ChemistryBSC-IChemistry
317CH-03 Physical ChemistryBSC-IChemistry
318CH-03 Physical ChemistryBSC-IChemistry
319CH-03 Physical ChemistryBScA(CH)Chemistry
320CH-04 Practical ChemistryBScA(CH)Chemistry
321CH-04 Practical ChemistryBSC-IChemistry
322CH-04 Practical ChemistryBSC-IChemistry
323CH-04 Practical ChemistryBSC-IChemistry
324CH-04 Practical ChemistryBSC-IChemistry
325CH-05 Inorganic ChemistryBSC-IIChemistry
326CH-05 Inorganic ChemistryBSC-IIChemistry
327CH-05 Inorganic ChemistryBSC-IIChemistry
328CH-05 Inorganic ChemistryBSC-IIChemistry
329CH-05 Inorganic ChemistryBSC-IIChemistry
330CH-05 Inorganic ChemistryBSC-IIChemistry
331CH-05 Inorganic ChemistryBSC-IIChemistry
332CH-05 Inorganic ChemistryBSC-IIChemistry
333CH-05 Inorganic ChemistryBSC-IIChemistry
334CH-05 Inorganic ChemistryBSC-IIChemistry
335CH-05 Inorganic ChemistryBSC-IIChemistry
336CH-05 Inorganic ChemistryBScA(CH)Chemistry
337CH-05 Inorganic ChemistryBSC2L-2Chemistry
338CH-05 Inorganic ChemistryBSC2L-2Chemistry
339CH-05 Inorganic ChemistryBSC2L-2Chemistry
340CH-05 Inorganic ChemistryBSC2L-2Chemistry
341CH-06 Organic ChemistryBSC2L-2Chemistry
342CH-06 Organic ChemistryBSC2L-2Chemistry
343CH-06 Organic ChemistryBSC2L-2Chemistry
344CH-06 Organic ChemistryBSC2L-2Chemistry
345CH-06 Organic ChemistryBScA(CH)Chemistry
346CH-06 Organic ChemistryBSC-IIChemistry
347CH-06 Organic ChemistryBSC-IIChemistry
348CH-06 Organic ChemistryBSC-IIChemistry
349CH-06 Organic ChemistryBSC-IIChemistry
350CH-06 Organic ChemistryBSC-IIChemistry
351CH-06 Organic ChemistryBSC-IIChemistry
352CH-06 Organic ChemistryBSC-IIChemistry
353CH-06 Organic ChemistryBSC-IIChemistry
354CH-06 Organic ChemistryBSC-IIChemistry
355CH-06 Organic ChemistryBSC-IIChemistry
356CH-06 Organic ChemistryBSC-IIChemistry
357CH-07 Physical ChemistryBSC-IIChemistry
358CH-07 Physical ChemistryBSC-IIChemistry
359CH-07 Physical ChemistryBSC-IIChemistry
360CH-07 Physical ChemistryBSC-IIChemistry
361CH-07 Physical ChemistryBSC-IIChemistry
362CH-07 Physical ChemistryBSC-IIChemistry
363CH-07 Physical ChemistryBSC-IIChemistry
364CH-07 Physical ChemistryBSC-IIChemistry
365CH-07 Physical ChemistryBSC-IIChemistry
366CH-07 Physical ChemistryBSC-IIChemistry
367CH-07 Physical ChemistryBSC-IIChemistry
368CH-07 Physical ChemistryBScA(CH)Chemistry
369CH-07 Physical ChemistryBSC2L-2Chemistry
370CH-07 Physical ChemistryBSC2L-2Chemistry
371CH-07 Physical ChemistryBSC2L-2Chemistry
372CH-07 Physical ChemistryBSC2L-2Chemistry
373CH-08 Practical ChemistryBSC2L-2Chemistry
374CH-08 Practical ChemistryBSC2L-2Chemistry
375CH-08 Practical ChemistryBSC2L-2Chemistry
376CH-08 Practical ChemistryBSC2L-2Chemistry
377CH-08 Practical ChemistryBScA(CH)Chemistry
378CH-08 Practical ChemistryBSC-IIChemistry
379CH-08 Practical ChemistryBSC-IIChemistry
380CH-08 Practical ChemistryBSC-IIChemistry
381CH-08 Practical ChemistryBSC-IIChemistry
382CH-08 Practical ChemistryBSC-IIChemistry
383CH-08 Practical ChemistryBSC-IIChemistry
384CH-08 Practical ChemistryBSC-IIChemistry
385CH-08 Practical ChemistryBSC-IIChemistry
386CH-08 Practical ChemistryBSC-IIChemistry
387CH-08 Practical ChemistryBSC-IIChemistry
388CH-08 Practical ChemistryBSC-IIChemistry
389CH-09 Inorganic ChemistryBSC-IIIChemistry
390CH-09 Inorganic ChemistryBSC-IIIChemistry
391CH-09 Inorganic ChemistryBSC-IIIChemistry
392CH-09 Inorganic ChemistryBSC-IIIChemistry
393CH-09 Inorganic ChemistryBSC-IIIChemistry
394CH-09 Inorganic ChemistryBSC-IIIChemistry
395CH-09 Inorganic ChemistryBSC-IIIChemistry
396CH-09 Inorganic ChemistryBSC-IIIChemistry
397CH-09 Inorganic ChemistryBSC-IIIChemistry
398CH-09 Inorganic ChemistryBSC-IIIChemistry
399CH-09 Inorganic ChemistryBSC-IIIChemistry
400CH-09 Inorganic ChemistryBSC2L-3Chemistry
401CH-09 Inorganic ChemistryBScA(CH)Chemistry
402CH-09 Inorganic ChemistryBSC3L-3Chemistry
403CH-09 Inorganic ChemistryBSC3L-3Chemistry
404CH-09 Inorganic ChemistryBSC2L-3Chemistry
405CH-09 Inorganic ChemistryBSC2L-3Chemistry
406CH-09 Inorganic ChemistryBSC3L-3Chemistry
407CH-09 Inorganic ChemistryBSC2L-3Chemistry
408CH-09 Inorganic ChemistryBSC2L-3Chemistry
409CH-09 Inorganic ChemistryBSC3L-3Chemistry
410CH-10 Organic ChemistryBSC3L-3Chemistry
411CH-10 Organic ChemistryBSC2L-3Chemistry
412CH-10 Organic ChemistryBSC2L-3Chemistry
413CH-10 Organic ChemistryBSC3L-3Chemistry
414CH-10 Organic ChemistryBSC2L-3Chemistry
415CH-10 Organic ChemistryBSC2L-3Chemistry
416CH-10 Organic ChemistryBSC3L-3Chemistry
417CH-10 Organic ChemistryBSC3L-3Chemistry
418CH-10 Organic ChemistryBScA(CH)Chemistry
419CH-10 Organic ChemistryBSC2L-3Chemistry
420CH-10 Organic ChemistryBSC-IIIChemistry
421CH-10 Organic ChemistryBSC-IIIChemistry
422CH-10 Organic ChemistryBSC-IIIChemistry
423CH-10 Organic ChemistryBSC-IIIChemistry
424CH-10 Organic ChemistryBSC-IIIChemistry
425CH-10 Organic ChemistryBSC-IIIChemistry
426CH-10 Organic ChemistryBSC-IIIChemistry
427CH-10 Organic ChemistryBSC-IIIChemistry
428CH-10 Organic ChemistryBSC-IIIChemistry
429CH-10 Organic ChemistryBSC-IIIChemistry
430CH-10 Organic ChemistryBSC-IIIChemistry
431CH-11 Physical ChemistryBSC-IIIChemistry
432CH-11 Physical ChemistryBSC-IIIChemistry
433CH-11 Physical ChemistryBSC-IIIChemistry
434CH-11 Physical ChemistryBSC-IIIChemistry
435CH-11 Physical ChemistryBSC-IIIChemistry
436CH-11 Physical ChemistryBSC-IIIChemistry
437CH-11 Physical ChemistryBSC-IIIChemistry
438CH-11 Physical ChemistryBSC-IIIChemistry
439CH-11 Physical ChemistryBSC-IIIChemistry
440CH-11 Physical ChemistryBSC-IIIChemistry
441CH-11 Physical ChemistryBSC-IIIChemistry
442CH-11 Physical ChemistryBSC2L-3Chemistry
443CH-11 Physical ChemistryBScA(CH)Chemistry
444CH-11 Physical ChemistryBSC3L-3Chemistry
445CH-11 Physical ChemistryBSC3L-3Chemistry
446CH-11 Physical ChemistryBSC2L-3Chemistry
447CH-11 Physical ChemistryBSC2L-3Chemistry
448CH-11 Physical ChemistryBSC3L-3Chemistry
449CH-11 Physical ChemistryBSC2L-3Chemistry
450CH-11 Physical ChemistryBSC2L-3Chemistry
451CH-11 Physical ChemistryBSC3L-3Chemistry
452CH-12 Practical ChemistryBSC3L-3Chemistry
453CH-12 Practical ChemistryBSC2L-3Chemistry
454CH-12 Practical ChemistryBSC2L-3Chemistry
455CH-12 Practical ChemistryBSC3L-3Chemistry
456CH-12 Practical ChemistryBSC2L-3Chemistry
457CH-12 Practical ChemistryBSC2L-3Chemistry
458CH-12 Practical ChemistryBSC3L-3Chemistry
459CH-12 Practical ChemistryBSC3L-3Chemistry
460CH-12 Practical ChemistryBScA(CH)Chemistry
461CH-12 Practical ChemistryBSC2L-3Chemistry
462CH-12 Practical ChemistryBSC-IIIChemistry
463CH-12 Practical ChemistryBSC-IIIChemistry
464CH-12 Practical ChemistryBSC-IIIChemistry
465CH-12 Practical ChemistryBSC-IIIChemistry
466CH-12 Practical ChemistryBSC-IIIChemistry
467CH-12 Practical ChemistryBSC-IIIChemistry
468CH-12 Practical ChemistryBSC-IIIChemistry
469CH-12 Practical ChemistryBSC-IIIChemistry
470CH-12 Practical ChemistryBSC-IIIChemistry
471CH-12 Practical ChemistryBSC-IIIChemistry
472CH-12 Practical ChemistryBSC-IIIChemistry
473CHR-1 Human Rights in International ScenarioCHRNot Applicable
474CHR-2 Human Rights and IndiaCHRNot Applicable
475CHR-3 Emerging Issues and Judicial ApproachesCHRNot Applicable
476CIA-01 Ayurved PrabodhCIANot Applicable
477CIA-02 Chikitsa Vigyan evam PanchkarmCIANot Applicable
478CIJ-01 Falit Jyotish ka Saidhantik GyanCIJNot Applicable
479CIJ-02 Jyotish dwara Faladesh ki VidhiCIJNot Applicable
480CLP-1 Consumerism,Consumer and the Law in IndiaCLPNot Applicable
481CLP-2 Consumer Practices and ProceduresCLPNot Applicable
482CLP-3 EssayCLPNot Applicable
483CPGM-01 Gandhi : Life and ThoughtCPGMNot Applicable
484CPGM-02 Gandhi : TechniquiesCPGMNot Applicable
485CPNM-01 Mahatma Gandhi Narega AdhiniyamCPNMNot Applicable
486CPNM-02 Met : Karya evam DayitvaCPNMNot Applicable
487CS-01 Computer Applications in Corporate WorldBSC-IComputer Science
488CS-01 Computer Applications in Corporate World BA-IComputer Science
489CS-02 Web Authoring ToolsBA-IComputer Science
490CS-02 Web Authoring ToolsBSC-IComputer Science
491CS-03 Practical in Web Authoring ToolsBSC-IComputer Science
492CS-03 Practical in Web Authoring ToolsBA-IComputer Science
493CS-04 Operating SystemBSC-IIComputer Science
494CS-04 Operating SystemBSC-IIComputer Science
495CS-04 Operating SystemBSC-IIComputer Science
496CS-04 Operating SystemBSC-IIComputer Science
497CS-04 Operating SystemBSC-IIComputer Science
498CS-04 Operating SystemBA2L-2Computer Science
499CS-04 Operating SystemBA-IIComputer Science
500CS-04 Operating SystemBA2L-2Computer Science
501CS-04 Operating SystemBSC2L-2Computer Science
502CS-05 Programming in CBSC2L-2Computer Science
503CS-05 Programming in CBA2L-2Computer Science
504CS-05 Programming in CBA-IIComputer Science
505CS-05 Programming in CBA2L-2Computer Science
506CS-05 Programming in CBSC-IIComputer Science
507CS-05 Programming in CBSC-IIComputer Science
508CS-05 Programming in CBSC-IIComputer Science
509CS-05 Programming in CBSC-IIComputer Science
510CS-05 Programming in CBSC-IIComputer Science
511CS-06 Practical in Programming in CBSC-IIComputer Science
512CS-06 Practical in Programming in CBSC-IIComputer Science
513CS-06 Practical in Programming in CBSC-IIComputer Science
514CS-06 Practical in Programming in CBSC-IIComputer Science
515CS-06 Practical in Programming in CBSC-IIComputer Science
516CS-06 Practical in Programming in CBA2L-2Computer Science
517CS-06 Practical in Programming in CBA-IIComputer Science
518CS-06 Practical in Programming in CBA2L-2Computer Science
519CS-06 Practical in Programming in CBSC2L-2Computer Science
520CS-07 Database Management SystemBA3L-3Computer Science
521CS-07 Database Management SystemBA2L-3Computer Science
522CS-07 Database Management SystemBA3L-3Computer Science
523CS-08 Programming in JAVABA3L-3Computer Science
524CS-08 Programming in JAVABA2L-3Computer Science
525CS-08 Programming in JAVABA3L-3Computer Science
526CS-09 Practical in JAVABA3L-3Computer Science
527CS-09 Practical in JAVABA2L-3Computer Science
528CS-09 Practical in JAVABA3L-3Computer Science
529CTE-01 Spoken English : Phonetics and PhonologyCTENot Applicable
530CTE-02 Written English : Morphology and MorphemesCTENot Applicable
531DAL-05 ProjectDALNot Applicable
532DCT-01 Bharat ke Nivasiyon ki Pramparao evam Sanskriti ki Ek RooprekhaDCTNot Applicable
533DCT-02 Rajasthan ke Nivasiyon ki Pramparayein tatha Sanskriti Paryatan Vishayak Paripreksheya(Rajya tatha SDCTNot Applicable
534DCT-03 Rajasthan ke Nivasiyon ki Paramparayein tatha Pray atan Pariprekshay(kala evam Shilp)DCTNot Applicable
535DCT-04 Rajasthan mein Yatra va ParyatanDCTNot Applicable
536DGA-01 Crop ProductionDGANot Applicable
537DGA-02 HorticultureDGANot Applicable
538DGA-03 Plant Protection ManagementDGANot Applicable
539DGA-04 Soil Fertility & Fertilizer UseDGANot Applicable
540DGA-04 Soil Fertility & Fertilizer UseDGALNot Applicable
541DGA-05 Agriculture Marketing & Extension EducationDGALNot Applicable
542DGA-05 Agriculture Marketing & Extension EducationDGANot Applicable
543DLIS-01 Pustkalya Vargikaran evam Suchikaran SiddhantDLISNot Applicable
544DLIS-02 Pustkalya Vargikaran -PrayogikDLISNot Applicable
545DLIS-03 Pustkalya Suchikaran -PrayogikDLISNot Applicable
546DLIS-04 Pustkalya Nityacharya evam Sandarbh KaryaDLISNot Applicable
547DLIS-05 Vidhayalay evam Sarvjanik PustkalyaDLISNot Applicable
548DLIS-06 Suchna Sevayein evam Computer AnuprayogDLISNot Applicable
549DMC-01 Mass Communication & Journalism : An IntroductionDMCNot Applicable
550DMC-02 Advertising and Public RelationsDMCNot Applicable
551DMC-03 Electronic MediaDMCNot Applicable
552DMC-04 Media LawsDMCNot Applicable
553DMC-05 Practical PaperDMCNot Applicable
554DNS-01 Prakritik Chikitasha Evam Vaikalpik Rogopchar VidhiyaDNSNot Applicable
555DNS-02 Manav Shareer Rachana Evam Kriya VigyanDNSNot Applicable
556DNS-03 Aahaar Evam Poshan VigyanDNSNot Applicable
557DNS-04 Prakritik Chikitasha Dwara Samanya Rogo ka UpchaarDNSNot Applicable
558DPL-05 ProjectDPLNot Applicable
559DSPR-01 Mahila Prastithi Vividh AayamDSPRNot Applicable
560DSPR-02 Samajik Samasayaein evam VyasanDSPRNot Applicable
561DSPR-03 Samajik Samasayaein Rajya Prashan evam Samajik ParivaritanDSPRNot Applicable
562DWSM-01 Jalgrahan Vikas-Sidhant evam RannitiDWSMNot Applicable
563DWSM-02 Jalgrahan Vikas Gatividhiyan-Aayojan CharanDWSMNot Applicable
564DWSM-03 Jalgrahan Vikas Kriyanvan CharanDWSMNot Applicable
565DWSM-04 Jalgrahan Prabandhan evam field workDWSMNot Applicable
566DYS-01 Sampurna Yoga ParichayaDYSNot Applicable
567DYS-02 Manav Shareer Rachana Evam Kriya VigyanDYSNot Applicable
568DYS-03 Aahaar Evam Poshan VigyanDYSNot Applicable
569DYS-04 Hat Yoga Evam Yoga VigyanDYSNot Applicable
570EC-01 Micro Economic TheoryBAA(EC)Economics
571EC-01 Micro Economic TheoryBScA(EC)Economics
572EC-01 Micro Economic TheoryBA-IEconomics
573EC-01 Micro Economic TheoryBSC-IEconomics
574EC-01 Micro Economic TheoryBA-IEconomics
575EC-02 Issues in Indian Economic DevelopmentBA-IEconomics
576EC-02 Issues in Indian Economic DevelopmentBSC-IEconomics
577EC-02 Issues in Indian Economic DevelopmentBA-IEconomics
578EC-02 Issues in Indian Economic DevelopmentBScA(EC)Economics
579EC-02 Issues in Indian Economic DevelopmentBAA(EC)Economics
580EC-03 Macro EconomicsBAA(EC)Economics
581EC-03 Macro EconomicsBScA(EC)Economics
582EC-03 Macro EconomicsBA-IIEconomics
583EC-03 Macro EconomicsBA2L-2Economics
584EC-03 Macro EconomicsBSC2L-2Economics
585EC-03 Macro EconomicsBSC-IIEconomics
586EC-03 Macro EconomicsBA-IIEconomics
587EC-03 Macro EconomicsBSC-IIEconomics
588EC-03 Macro EconomicsBA2L-2Economics
589EC-04 Economy of RajasthanBA2L-2Economics
590EC-04 Economy of RajasthanBSC-IIEconomics
591EC-04 Economy of RajasthanBA-IIEconomics
592EC-04 Economy of RajasthanBSC-IIEconomics
593EC-04 Economy of RajasthanBSC2L-2Economics
594EC-04 Economy of RajasthanBA2L-2Economics
595EC-04 Economy of RajasthanBA-IIEconomics
596EC-04 Economy of RajasthanBScA(EC)Economics
597EC-04 Economy of RajasthanBAA(EC)Economics
598EC-05 Quantitative MethodsBAA(EC)Economics
599EC-05 Quantitative MethodsBScA(EC)Economics
600EC-05 Quantitative MethodsBA-IIIEconomics
601EC-05 Quantitative MethodsBA3L-3Economics
602EC-05 Quantitative MethodsBA2L-3Economics
603EC-05 Quantitative MethodsBSC2L-3Economics
604EC-05 Quantitative MethodsBSC3L-3Economics
605EC-05 Quantitative MethodsBSC-IIIEconomics
606EC-05 Quantitative MethodsBA2L-3Economics
607EC-05 Quantitative MethodsBSC2L-3Economics
608EC-05 Quantitative MethodsBA-IIIEconomics
609EC-05 Quantitative MethodsBSC-IIIEconomics
610EC-05 Quantitative MethodsBA3L-3Economics
611EC-06 Money, Banking & Public FinanceBA3L-3Economics
612EC-06 Money, Banking & Public FinanceBSC-IIIEconomics
613EC-06 Money, Banking & Public FinanceBA-IIIEconomics
614EC-06 Money, Banking & Public FinanceBSC2L-3Economics
615EC-06 Money, Banking & Public FinanceBA2L-3Economics
616EC-06 Money, Banking & Public FinanceBSC-IIIEconomics
617EC-06 Money, Banking & Public FinanceBSC3L-3Economics
618EC-06 Money, Banking & Public FinanceBSC2L-3Economics
619EC-06 Money, Banking & Public FinanceBA2L-3Economics
620EC-06 Money, Banking & Public FinanceBA3L-3Economics
621EC-06 Money, Banking & Public FinanceBA-IIIEconomics
622EC-06 Money, Banking & Public FinanceBScA(EC)Economics
623EC-06 Money, Banking & Public FinanceBAA(EC)Economics
624ED-01 Understanding EducationBAA(ED)Education
625ED-01 Understanding EducationBA-IEducation
626ED-01 Understanding EducationBA-IEducation
627ED-02 Understanding LearnerBA-IEducation
628ED-02 Understanding LearnerBA-IEducation
629ED-02 Understanding LearnerBAA(ED)Education
630ED-03 Education and DevelopmentBAA(ED)Education
631ED-03 Education and DevelopmentBA-IIEducation
632ED-03 Education and Development

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