Optimizing Delay for MAC in Randomly Distributed Wireless Sensor Networks

Sikandar, Ajay; Kumar, Sushil; Maurya, Ghyani Umesh Kumar

doi:10.1007/978-81-322-1665-0_60

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…If rate of data generation is p where p ൌ then in W time pW data packet will be generated at each sensor. So on average each sensor will be served for 1 మ µሺµିሻ packets in each access, if we have to send N packets on average it will take time as follows, [1] T…”

Section: Delay Analysis Without Memorymentioning

confidence: 99%

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Performance Analysis of Channel Access Model for MAC in Randomly Distributed Wireless Sensor Networks

Sikandar¹,

Kumar²

2014

IJCNC

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Medium Access control (MAC) is one of the fundamental problems in wireless sensor networks. The performance of wireless sensor network depends on it. The main objective of a medium access control method is to provide high throughput, minimize the delay, and conservers the energy consumption by avoiding the collisions. In this paper, a general model for MAC protocol to reduce the delay, maximize throughput and conserve the energy consumption in channel accessing in high density randomly distributed wireless sensor network is presented. The proposed model is simulated using MATLAB. The simulation results show that the average delay for sensors with sufficient memory is lower than sensors without memory. Further, the throughput of the channel access method with memory is better than without memory.

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Section: Delay Analysis Without Memorymentioning

confidence: 99%

“…In planned deployment, all or some of the sensor nodes are to be deployed in well calculated ways and also maintenance in such type of network is comparatively easy. There are many important issues such as routing, connectivity, localization and channel access control [1,2,3].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Channel Access Model for MAC in Randomly Distributed Wireless Sensor Networks

Sikandar¹,

Kumar²

2014

IJCNC

Self Cite

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“…Although there are many applications of Markov chains in WSNs, such as data aggregation and routing [11], [12], duty cycle [13], sensing coverage [14], target tracking [15]- [17], MAC backoff operation [18], [19], and security [20]- [22], this paper focuses only on the applications of MDPs in WSNs. The main difference between an MDP and a Markov chain is that the Markov chain does not consider actions and rewards.…”

Section: Introductionmentioning

confidence: 99%

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Alsheikh

Hoang

Niyato

et al. 2015

IEEE Commun. Surv. Tutorials

176

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Abstract-Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.

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“…Once the sensors are deployed in such environment, their batteries cannot be recharged or replaced. Therefore, to prolong the network lifetime, energy efficient algorithm need to be developed [1,2]. Due to their limited battery power, energy consumption is a key issue in designingthe network protocols and applications of WSNs.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient clustering in Heterogeneous Wireless Sensor Networks using Degree of Connectivity

Sikandar¹,

Kumar²

2015

IJCNC

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Optimizing Delay for MAC in Randomly Distributed Wireless Sensor Networks

Cited by 3 publications

References 4 publications

Performance Analysis of Channel Access Model for MAC in Randomly Distributed Wireless Sensor Networks

Performance Analysis of Channel Access Model for MAC in Randomly Distributed Wireless Sensor Networks

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Energy Efficient clustering in Heterogeneous Wireless Sensor Networks using Degree of Connectivity

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