Energy-efficient computing for wildlife tracking

Future Internet

Mobility trace techniques makes possible drawing the behaviors of real-life movement which shape wireless networks mobility whereabouts. In our investigation, several trace mobility models have been collected after the devices’ deployment. The main issue of this classical procedure is that it produces uncompleted records due to several unpredictable problems occurring during the deployment phase. In this paper, we propose a new procedure aimed at collecting traces while deployment phase failures are avoided, which improves the reliability of data. The introduced procedure makes possible the complete generation of traces with a minimum amount of damage without the need to recover mobile devices or lose them, as it is the case in previous mobility traces techniques. Based on detecting and correcting all accidental issues in real time, the proposed trace scanning offers a set of relevant information about the vehicle status which was collected during seven months. Furthermore, the prop...

Handbook of Research on Cloud Computing and Big Data Applications in IoT, 2019

Opportunistic networks are one of the emerging evolutions of the network system. In opportunistic networks, nodes are able to communicate with each other even if the route between source to destination does not already exist. Opportunistic networks have to be delay tolerant in nature (i.e., able to tolerate larger delays). Delay tolerant network (DTNs) uses the concept of “store-carry-forward” of data packets. DTNs are able to transfer data or establish communication in remote area or crisis environment where there is no network established. DTNs have many applications like to provide low-cost internet provision in remote areas, in vehicular networks, noise monitoring, extreme terrestrial environments, etc. It is therefore very promising to identify aspects for integration and inculcation of opportunistic network methodologies and technologies into delay tolerant networking. In this chapter, the authors emphasize delay tolerant networks by considering its architectural, routing, con...

ACM Transactions on Internet Technology

Mobile opportunistic networking is a promising technology that can supplement existing cellular and WiFi networks to provide desirable services for smart and connected communities. Message routing is the most compelling challenge in mobile opportunistic networks due to the lack of contemporaneous end-to-end paths and the resource constraints at mobile devices. To improve the probability of successful message delivery, most existing routing schemes use the past contact history to predict future contacts for message forwarding, and exploit message replication and redundancy for multicopy routing. However, most existing predictionbased routing schemes simply use the average pairwise contact probability as the routing metric and neglect the benefits of exploring fine-grained contact information such as pairwise repeated contact patterns to improve the accuracy of predicting future contacts. Moreover, there is no efficient mechanism that can adaptively control message replication in a decentralized manner to achieve both high probability of successful message delivery and low message overhead. To address these problems, we present FGAR, a routing protocol designed for mobile opportunistic networks by leveraging fine-grained contact characterization and adaptive message replication. In FGAR, contact history is characterized in a fine-grained manner with timing information using a sliding window mechanism, and future contacts are predicted based on the fine-grained contact information, thereby improving the accuracy of contact prediction. We further design an efficient message replication scheme in which message replication is controlled in a fully decentralized manner by taking into account the expected message delivery probability, the replication history, and the quality of the encountered device. A replica is generated only when it is necessary to fulfill the expected message delivery probability. We evaluate our scheme through trace-driven simulations, and the simulation results show that FGAR outperforms existing schemes. In comparison with PRoPHET, FGAR can achieve more than 20% improvement on average on successful message delivery, whereas the message overhead has been reduced by a factor up to 15.

ACM Transactions on Internet of Things, 2020

Sensing systems for long-term monitoring constitute an important part of the emerging Internet of Things. In this domain, energy harvesting and infrastructure-less communication enable truly autonomous and maintenance-free operation of sensor nodes gathering long-term environmental data. Due to the infrastructure-less nature of the communication, receivers are not always available. The variable energy provided by the environment and the receiver’s mobility lead to non-deterministic node availability. In this work, we study infrastructure-less data transmission schemes to optimize communication when both senders and receivers exhibit intermittent behavior. We rely on the notion of data utility, describing the importance of sensed data to the receiver, to determine an optimal communication scheme. Deriving the communication policy that maximizes the utility of the received data is shown to be a convex optimization problem. The resulting scheme is implemented and validated on a battery...

Sensors, 2018

Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the...