Abstract-Routing protocol in wireless sensor network (WSN) has always been a frequently adopted topic of research in WSN owing to many unsolved issues in it. This paper discusses about the multicast routing protocols in WSN and briefs up different forms of standard research contribution as well as significant recent research techniques toward leveraging the performance of multicast routing. The paper then discusses the beneficial factor and limiting factor in existing multicast techniques and highlights the research gap in this. In order to overcome the research gap, a novel architecture to address the optimization as a cost minimization problem associated with multicast routing in WSN is proposed. This paper contributes to show a present scenario of multicast routing performance in WSN and thereby assists the readers about the possible direction of future with clear visualization of system architecture.

Opportunistic networks (OppNets) have attracted widespread attention as wireless technologies have advanced. OppNets are widely used in delay‐tolerant applications because they route messages using a store‐carry‐forward mechanism. Recently, socially aware routing has been increasingly modeled for message dissemination in OppNets, where the message is routed selectively through cooperative nodes based on user interests; however, routing becomes extremely difficult as node density and data size increase. However, the current method fails to reduce data redundancy, message overhead, delay, and improve performance efficiency. To address the issues, this article proposes social context‐aware microscopic routing (SCAMR) for OppNets. SCAMR uses cluster‐based communication, novel social‐context association mapping, and an improved lost packet retrieval mechanism with minimal messaging overhead. In this work, the experiment was performed by considering three scenarios: varying node size, varying buffer size, and varying time‐to‐live size. The experimental results show that the SCAMR scheme improves delivery ratio by 71.25%, 67.87%, 69.18%, reduces delay by 33.93%, 26.68%, 35.36%, reduces the number of hop nodes (i.e., messaging overhead) by 77.84%, 71.53%, 76.04% over existing approaches namely, SCARF (SoCial‐Aware Reliable Forwarding Technique for Vehicular Communications), SRS (secure routing strategy), and EDT (effective data transmission) considering different scenarios, respectively.

The proliferations of IoT technologies and applications have led to an increased interest in Wireless Sensor Networks (and in particular, multi-hop networks). Wireless sensor networks are composed of small mobile terminals which have limited system resources. Due to this, these networks are vulnerable to changes in network status arising from changes in the network parameters such as, position / layout of sensors, signal strength, environmental conditions, etc. In addition, the network nodes are also constrained in terms of energy provided by the battery. It is an significant consideration to be accounted so as to prolong their operational time, since this adds to the network lifetime. Lot of research has gone into routing and transmission technologies for wireless sensor networks. Conventional routing mechanisms for WSNs still suffer from energy-hole problem caused by difficulties in adaptive route management. Thus, it is imperative that efficient routing mechanisms be developed in order to conserve energy and improve network lifetime. One popular approach is to use meta-heuristic algorithms for optimal path selection in a WSN route management system. A very popular meta-heuristic algorithm used for this objective is the Ant Colony Optimization (ACO) algorithms. ACO has been used as a base for many routing management systems. In this paper an extensive analysis of the performance of ACO based route selection mechanism is reported and also reporting a comparative analysis of efficacy of the ACO routing algorithm over the standard Greedy algorithm in finding routes with different count of sensor nodes and different count of ants. Then find that the ACO routing algorithm outdoes the Greedy algorithm with respect to the number of routes identified.