Junaid Ahmed Khan

Energy management in Wireless Sensor Networks

Energy management in Wireless Sensor Networks (WSNs) is of paramount importance for the remotely deployed energy stringent sensor nodes. These nodes are typically powered by attached batteries. Several battery-driven energy conservation schemes are proposed to ensure energy efficient network operation. The constraints associated to the limited battery capacity shifted the research trend towards finding alternate sources by harvesting ambient energy. This survey presents a high level taxonomy of energy management in WSNs. We analyze different battery-driven energy consumption based schemes and energy harvesting based energy provisioning schemes. We also highlight the recent breakthrough of wireless energy transference to a sensor node as an alternative to typical batteries. We recommend taking into account recent energy provisioning advancements in parallel with the traditional energy conservation approaches for a sensor network while designing energy efficient schemes.

TRW: An energy storage capacity model for energy harvesting sensors in wireless sensor networks

Energy provisioning trend in Wireless Sensor Networks (WSNs) is shifted towards alternate sources by utilizing available ambient energy, of which solar irradiance harvesting is considered a viable alternative to fixed batteries. However, the energy storage buffer for harvested solar energy should be adaptive to the sporadic nature of the diurnal solar radiation availability. We believe that the typical fixed battery models no longer apply in harvesting enabled sensors. Therefore, we propose a random walk based stochastic model namely; Trinomial Random Walk (TRW) model for the storage capacity of harvesting enabled sensors. We then apply the proposed model on a comprehensive solar radiation data set of four different locations around the globe. Our performance evaluation demonstrates that the proposed model better analyze the sporadic nature of the diurnal solar radiation availability for estimating the required storage capacity. We further investigate an optimal power consumption value for a given energy store size, such that the utilization of harvested energy is maximized and the probability of energy depletion is minimized. For a given energy harvesting scenario, our model better approximates the optimal load with probability of up to a maximum of 98%, compared to a maximum of 37% for the binomial random walk model.

STRIVE: Socially-Aware Three-Tier Routing in Information-Centric Vehicular Environment

Content distribution in vehicular networks is greatly impaired by high mobility and intermittent connectivity. Social-aware content distribution schemes based on typical centrality metrics address the challenge, however they suffer due to their network-centric nature instead of information centric. We suggest to exploit the recently proposed information-centric networking architecture which cater the issue by decoupling host-user and support in-network caching at intermediate nodes. In this paper, we propose a novel information-centric social-aware content distribution protocol, STRIVE confining the broadcast nature of interest/content by routing it selectively towards high centrality information facilitator vehicles. We use a three-tier forwarding strategy to discover potential local and global information facilitators in an urban environment for efficient content delivery. The performance evaluation implements a scalable simulation environment deploying up to 2986 vehicles using realistic vehicular mobility traces. Simulation results show that our proposed novel vehicle centrality based content distribution protocol, STRIVE outperforms existing social content distribution metrics used in the literature.

Autonomous Identification and Optimal Selection of Popular Smart Vehicles for Urban Sensing - An Information-centric Approach

Today, vehicles are becoming powerful sensor platforms capable of collecting, storing, and sharing large amounts of sensory data by constant monitoring of urban streets. It is quite challenging to upload such data from all vehicles to the infrastructure due to limited bandwidth resources and high cost. This invokes the need to identify the appropriate vehicles, important for different urban sensing tasks based on their natural mobility. This paper address this problem of leveraging the self-decision making ability of a “smart vehicle” to measure its relative importance in the network. To do so, we present InfoRank as an information-centric algorithm for a vehicle to first autonomously rank different location-aware information. It then uses the information importance along its mobility pattern to find its importance in the network. We also present a selection algorithm to find the best ranked vehicles for urban sensing and vicinity monitoring to achieve a desired coverage within a limited budget. Our vehicle ranking system is the first step toward identifying the best information hubs to be used in the network for the efficient collection, storage, and distribution of urban sensory information. We evaluate InfoRank under a scalable simulation environment using realistic vehicular mobility traces. Results show that the proposed ranking system efficiently identified socially important vehicles in comparison to other ranking schemes.

Improving multipath data delivery and energy efficiency in few disjoint paths scenario

Multipath data transmission is an established mechanism to ensure reliable data delivery in mission critical sensor networks. Advantages of a multipath data delivery protocol can be realized if there exist large number of paths in the network. However, this is generally not the case as finding alternative paths is an expensive process. Therefore, we propose minor modifications to a basic multipath approach so that it does not require large number of multipaths. We augment multipath approach with only one per hop retransmission. Through analytical models and trace driven simulations, we show that the data delivery probability of the proposed approach is generally higher than the multipath approach. We also show that the proposed approach consumes upto 50% less energy than the basic multipath approach.

On the Energy Efficiency and Performance of Neighbor Discovery Schemes for Low Duty Cycle IoT Devices

Mobile sensing and proximity-based applications require smart devices to find other nodes in vicinity, though it is challenging for a device to find neighbors in an energy efficient manner while running on low duty cycles. Neighbor discovery schemes allow nodes to follow a schedule to become active and send beacons or listen for other active nodes in order to discover each other with a bounded latency. However, a trade-off exists between the energy consumption and the time a node takes to discover neighbors using a given activity schedule. Moreover, energy consumption is not the only bottleneck, as theoretically perfect schedules can result in discovery failures in a real environment. In this paper, we provide an in-depth study on neighbor discovery, by first defining the relation between energy efficiency, discovery latency and the fraction of discovered neighbors. We evaluate existing mechanisms using extensive simulations for up to 100 nodes and testbed implementations for up to 15 nodes, with no synchronization between nodes and using duty cycles as low as 1% and 5%. Moreover, the literature assumes that multiple nodes active simultaneously always result in neighbor discovery, which is not true in practice as this can lead to collisions between the transmitted messages. Our findings reveal such scalability issues in existing schemes, where discovery fails because of collisions between beacons from multiple nodes active at the same time. Therefore, we show that energy efficient discovery schemes do not necessarily result in successful discovery of all neighbors, even when the activity schedules are computed in a deterministic manner.

Towards the ranking of important smart vehicles in VANETs - An information-centric approach

Vehicles today are equipped with various sensors and cameras to gather and share tremendous amount of heterogeneous data from urban streets. The challenge is to identify the appropriate vehicles from the fleet of vehicles, important for the collection, distribution and storage of such massive data. This paper addresses the autonomous identification of such “Smart Vehicles” without relying on the infrastructure network. Therefore, we propose “InfoRank” as an Information-centric algorithm for a vehicle to first rank different location-aware information associated to it. It then uses the information importance to analytically find its relative importance in the network. InfoRank is the first step towards identifying the best information hubs to be used in the network for the efficient collection, storage and distribution of urban sensory information. Results from scalable simulations using realistic vehicular mobility traces show that InfoRank is an efficient ranking algorithm to find popular information facilitator vehicles in comparison to other ranking metrics in the literature.