Network Lifetime and Coverage Fraction Analysis for Wireless Sensor Networks

285-291In Wireless Sensor Networks, two crucial parameters are lifetime of the network and optimal coverage for sensed region. This paper addresses the issues and challenges pertaining to these parameters for further investigation, and provides a method to approximate the energy utilization and optimal coverage inside the bottleneck zone for wireless sensor networks. The proposed analytical framework calculates correctly the network lifetime upper bound of wireless sensor networks. The derivation of the network lifetime upper bound is carried out using (i) network coding and (ii) network coding with duty cycle. Based on that, an approximate derivation is made and the corresponding results are obtained from the simulation study. The comparison of the results of the previous study and those obtained in this paper reveals that the actual network lifetime upper bound is lower in the present case. This is due to the assumption made by authors of previous work, on coder nodes’ presence throughout the bottleneck zone instead of only one hop distance away from the sink. In addition, the effect of coverage fraction in case of node failure, on network lifetime upper bound is derived for the previously reported and present model. The simulated results obtained from new derivation show that the coverage fraction is lesser than that obtained by previous model

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Energy saving and reliability for Wireless Body Sensor Networks (WBSN)

In healthcare and medical applications, the energy consumption of biosensor nodes affects the collection of biomedical data packets, which are sensed and measured from the human body and then transmitted toward the sink node. Nodes that are near to the sink node consume more energy as all biomedical packets are aggregated through these nodes when communicated to sink node. Each biosensor node in a wireless body sensor networks (WBSNs) such as ECG (Electrocardiogram), should provide accurate biomedical data due to the paramount importance of patient information. We propose a technique to minimise energy consumed by biosensor nodes in the bottleneck zone for WBSNs, which applies the Coordinated Duty Cycle Algorithm (CDCA) to all nodes in the bottleneck zone. Superframe order (SO) selection in CDCA is based on real traffic and the priority of the nodes in the WBSN. Furthermore, we use a special case of network coding, called Random Linear Network coding (RLNC), to encode the biomedical packets to improve reliability through calculating the probability of successful reception (PSR) at the sink node. It can be concluded that CDCA outperforms other algorithms in terms of energy saving as it achieves energy savings for most biosensor nodes in WBSNs. RLNC employs relay nodes to achieve the required level of reliability in WBSNs and to guarantee that the biomedical data is delivered correctly to the sink nod

A Method for Clustering and Cooperation in Wireless Multimedia Sensor Networks

Wireless multimedia sensor nodes sense areas that are uncorrelated to the areas covered by radio neighbouring sensors. Thus, node clustering for coordinating multimedia sensing and processing cannot be based on classical sensor clustering algorithms. This paper presents a clustering mechanism for Wireless Multimedia Sensor Networks (WMSNs) based on overlapped Field of View (FoV) areas. Overlapping FoVs in dense networks cause the wasting of power due to redundant area sensing. The main aim of the proposed clustering method is energy conservation and network lifetime prolongation. This objective is achieved through coordination of nodes belonging to the same cluster to perform assigned tasks in a cooperative manner avoiding redundant sensing or processing. A paradigm in this concept, a cooperative scheduling scheme for object detection, is presented based on the proposed clustering method

Enhancement of the duty cycle cooperative medium access control for wireless body area networks

This paper presents a novel energy-efficient and reliable connection to enhance the transmission of data over a shared medium for wireless body area networks (WBAN). We propose a novel protocol of two master nodes-based cooperative protocol. In the proposed protocol, two master nodes were considered, that is, the belt master node and the outer body master node. The master nodes work cooperatively to avoid the retransmission process by sensors due to fading and collision, reducing the bit error rate (BER), which results in a reduction of the duty cycle and average transmission power. In addition, we have also presented a mathematical model of the duty cycle with the proposed protocol for the WBAN. The results show that the proposed cooperative protocol reduced the BER by a factor of 4. The average transmission power is reduced by a factor of 0.21 and this shows the potential of the proposed technique to be used in future wearable wireless sensors and systems