Cross Layered Network Condition Aware Mobile-Wireless Multimedia Sensor Network Routing Protocol for Mission Critical Communication

The high pace emergence in wireless technologies have given rise to an immense demand towards Quality of Service (QoS) aware multimedia data transmission over mobile wireless multimedia sensor network (WMSN). Ensuring reliable communication over WMSN while fulfilling timely and optimal packet delivery over WMSN can be of great significance for emerging IoT ecosystem. With these motivations, in this paper a highly robust and efficient cross layered routing protocol named network condition aware mobile-WMSN routing protocol (NCAM-RP) has been developed. NCAM-RP introduces a proactive neighbour table management, congestion awareness, packet velocity estimation, dynamic link quality estimation (DLQE), and deadline sensitive service differentiation based multimedia traffic prioritization, and multi-constraints based best forwarding node selection mechanisms. These optimization measures have been applied on network layer, MAC layer and the physical layer of the protocol stack that eventually strengthen NCAM-RP to enable QoS-aware multimedia data transmission over WMSNs. The proposed NCAM-RP protocol intends to optimize real time mission critical (even driven) multimedia data (RTMD) transmission while ensuring best feasible resource allocation to the non-real time (NRT) data traffic over WMSNs. NCAM-RP has outperform RPAR based routing scheme in terms of higher data delivery, lower packet drops and deadline miss ratio. It signifies that NCAM-RP can ensure minimal retransmission that eventually can reduce energy consumption, delay and computational overheads. Being the mobility based WMSN protocol, NCAM-RP can play significant role in IoT ecosystem

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

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

DISTRIBUTED MULTI-HOP ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORKS

In a Wireless Sensor Network (WSN), routing is the process of finding a cost-effective route in terms of power consumption. As an evaluation criterion for the WSN performance, network lifetime is directly affected by the routing method. In a wide variety of WSNs, different techniques are used as routing methods, such as shortest distance path. In this paper, we propose a novel algorithm, optimizing power consumption in WSN nodes, based on the shortest path algorithm. In this approach, the energy level of nodes and their geographical distance from each other contribute to the weight of the connecting path. The proposed algorithm is used as a data dissemination method in WSNs with randomly scattered nodes. We also apply Dijkstra’s shortest path algorithm to the same networks. The results showed that the proposed algorithm increases the network lifetime up to 30 % by preventing nodes with low charge levels from early disconnection

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

DISTANCE AND HOPS-BASED ENERGY ESTIMATION IN WIRELESS SENSOR NETWORKS

The world of internet of things (IoT) and automation has been catching a robust pace to impact wide range of commercial and domestic applications for some time now. The IoT holds ad-hoc or wireless sensor networks (WSNs) at its very primary implementation level, the hardware level. The increasing requirement of these networks demands a renewed and better design of the network that improves the already existing setbacks of WSNs, which is mainly the power consumption and optimization. Routing highly affects the power consumed in the nodes in WSNs, hence having a modified routing algorithm which is specific to the application and meets its needs, particularly it is a good approach instead of having a generalized existent routing approach. Currently, for WSN having adequate number of nodes, routing involves maximum number of nodes and hops so as to reduce power consumption. However, for restricted areas and limited nodes, this scenario concludes with using up more number of nodes simultaneously resulting in reducing several batteries simultaneously. A routing algorithm is proposed in this paper for such applications that have a bounded region with limited resources. The work proposed in this paper is motivated from the routing algorithm positional attribute based next-hop determination approach (PANDA-TP) which proposes the increase in number of hops to reduce the requirement of transmission power. The aim of the proposed work is to compute the distance between the sending and receiving node and to measure the transmission power that would be required for a direct(path with minimum possible hops) and a multi-hop path. If the node is within the thresh-hold distance of the source, the packet is undoubtedly transferred directly; if the node is out of the thresh-hold distance, then the extra distance is calculated. Based on this, the power boosting factor for the source node, and if necessary, then the extra number of nodes that would be required to transmit is determined. An extra decision-making step is added to this approach which makes it convenient to utilize in varied situations. This routing approach suits the current level of robustness that the WSNs demand.Â