Table 1: Overview of existing protocols.

Scheme/featuresPerformance achievedFlawsComments

Energy-efficient communication protocol for wireless microsensor networks [4] Prolonged network lifetime and increased throughput. It is clustering based scheme that reduces global communication Clustering process consumes energy; moreover during CH selection energy consumption also increases. Selection of CH here depends upon the probability This scheme uses adaptive clustering and also the number of nodes in each cluster is not fixed. So, node density varies in each cluster. The cluster where node density is high load on CH increases for relaying data and it drains its energy quickly

Optimizing energy-latency trade-off in WSNs with controlled mobility [8] MS and data mules are used for multihop transmission. Latency decreases because this scheme uses optimal path for relaying data with the help of data mules Energy consumption increases for finding optimal route. Nodes that relay the data of nodes which are not in the transmission range of MS drain the energies quickly This scheme decreases data delivery latency on the cost of node energy which it uses for communication between them for finding optimal routes

iAM-DisCNT [12] Prolonged network lifetime. MS communicates and receives data from CHs The network has CH and MS. More energy is consumed during selection of CHs and CH node gets burden of relying data of member nodes Nodes send the data to MS through CHs; it increases delay in the network

Multiple Mobile Sink-based routing (MMSR) [13] It prolongs network lifetime as well as throughput of the network. It also reduces the energy consumption of nodes A maximum number of MSs increase the network cost This scheme balances the energy consumption by introducing the MSs in the network. If there are a sufficient number of MSs in the network, it can achieve the performance parameters. However, in the meantime network cost also increases

A study on cluster lifetime in multihop WSNs with cooperative MISO scheme [14] Prolonged network lifetime and minimizing fading through cooperation Energy consumed during clustering; also CH relay other CHs data and drain its energy quickly; thus its lifetime is short For network lifetime maximization linear aggregation is introduced, where after aggregation amount of data varies directly with the size of cluster

On maximizing the lifetime of WSNs in event-driven applications with MSs [15] MS, Cost of Tour (COT) is determined by convex optimization, whereas, in second part, tour region is bounded and problem is solved through heuristic algorithm End to end delay increases while applying the heuristics for finding the hop constrained trajectory to MS There are no constraints on the number of nodes in zones (ASNs); MS visit each zone for data gathering that increases delay

Greedy Maximum Residual Energy (GMRE), OPT, and Random Movement (RM) MS [5] Prolonged network lifetime in OPT, where sink locations are optimally defined in comparison with GMRE and RM Latency increases in the GMRE and OPT as compared to RM Overall due to mobility network lifetime is increased. Latency increases as nodes send data directly to MS

MobiRoute: routing towards a Mobile Sink for improving lifetime in WSN [17] Prolonged network lifetime, packet delivery ratio increases Energy consumption is greater as compared to static sink networkThis scheme considers very small scale network for larger networks where node density is high; it may not be such efficient

DDRP, MS, multihoping DDRP reduces the protocol overhead in comparison, prolonged network lifetime Energy consumed during relaying the data of other nodes, also in maintaining the routing table MS with multihoping increase data latency. Also, nodes consume more energy while relaying data of other nodes