Sensor networks have a significant potential in diverse applications some of which are already beginning to be deployed in areas such as environmental monitoring. As the application logic becomes more complex, programming difficulties are becoming a barrier to adoption of these networks. The difficulty in programming sensor networks is not only due to their inherently distributed nature but also the need for mechanisms to address their harsh operating conditions such as unreliable communications, faulty nodes, and extremely constrained resources. Researchers have proposed different programming models to overcome these difficulties with the ultimate goal of making programming easy while making full use of available resources. In this article, we first explore the requirements for programming models for sensor networks. Then we present a taxonomy of the programming models, classified according to the level of abstractions they provide. We present an evaluation of various programming models for their responsiveness to the requirements. Our results point to promising efforts in the area and a discussion of the future directions of research in this area.

Abstract-A data mule represents a mobile device that collects data in a sensor field by physically visiting the nodes in a sensor network. The data mule collects data when it is in the proximity of a sensor node. This can be an alternative to multihop forwarding of data when we can utilize node mobility in a sensor network. To be useful, a data mule approach needs to minimize data delivery latency. In this paper, we first formulate the problem of minimizing the latency in the data mule approach. The data mule scheduling (DMS) problem is a scheduling problem that has both location and time constraints. Then, for the 1D case of the DMS problem, we design an efficient heuristic algorithm that incorporates constraints on the data mule motion dynamics. We provide lower bounds of solutions to evaluate the quality of heuristic solutions. Through numerical experiments, we show that the heuristic algorithm runs fast and yields good solutions that are within 10 percent of the optimal solutions.

Abstract. Unlike traditional multihop forwarding among homogeneous static sensor nodes, use of mobile devices for data collection in wireless sensor networks has recently been gathering more attention. It is known that the use of mobility significantly reduces the energy consumption at each sensor, elongating the functional lifetime of the network, in exchange for increased data delivery latency. However, in previous work, mobility and communication capabilities are often underutilized, resulting in suboptimal solutions incurring unnecessarily large latency. In this paper, we focus on the problem of finding an optimal path of a mobile device, which we call "data mule," to achieve the smallest data delivery latency in the case of minimum energy consumption at each sensor, i.e., each sensor only sends its data directly to the data mule. We formally define the path selection problem and show the problem is N P-hard. Then we present an approximation algorithm and analyze its approximation factor. Numerical experiments demonstrate that our approximation algorithm successfully finds the paths that result in 10%-50% shorter latency compared to previously proposed methods, suggesting that controlled mobility can be exploited much more effectively.

With the increasing popularity of Cloud computing and Mobile computing, individuals, enterprises and research centers have started outsourcing their IT and computational needs to on-demand cloud services. Recently geographical load balancing techniques have been suggested for data centers hosting cloud computation in order to reduce energy cost by exploiting the electricity price differences across regions. However, these algorithms do not draw distinction among diverse requirements for responsiveness across various workloads. In this paper, we use the flexibility from the Service Level Agreements (SLAs) to differentiate among workloads under bounded latency requirements and propose a novel approach for cost savings for geographical load balancing. We investigate how much workload to be executed in each data center and how much workload to be delayed and migrated to other data centers for energy saving while meeting deadlines. We present an offline formulation for geographical load balancing problem with dynamic deferral and give online algorithms to determine the assignment of workload to the data centers and the migration of workload between data centers in order to adapt with dynamic electricity price changes. We compare our algorithms with the greedy approach and show that significant cost savings can be achieved by migration of workload and dynamic deferral with future electricity price prediction. We validate our algorithms on MapReduce traces and show that geographic load balancing with dynamic deferral can provide 20-30% cost-savings. I. INTRODUCTIONIncreasing energy prices and ability to dynamically track these price variations due to enhancements of the electrical grid raise the possibility of utilizing "cloud computing" for energy efficient computing. Energy efficiency in the cloud has been explored recently in [1], [2], [3], [4], [5]. While these explorations have suggested a number of hardware and software techniques for energy-savings considering different aspects, one non-conventional perspective is to utilize the predetermined service level agreements (SLAs) for energy efficiency. Often the specification of SLAs contains some flexibility which could be exploited to improve the performance and efficiency [6], [7]. One of the important performance metric for cloud-based services is latency in which service providers get a lot of interest. This paper leverages the latency requirements for energy efficient computing in the cloud.Naturally, energy efficiency in the cloud has been pursued in various ways including the use of renewable energy [8], [9] and improved scheduling algorithms [2], [5], [10], etc. Among them, improved scheduling algorithm is a promising approach for its broad applicability regardless of hardware configurations. The idea of utilizing SLA information to improve performance and efficiency is not entirely new. Recent work explores utilization of application deadline information for improving the performance of the applications (e.g. see [6], [7]). But the opportunities for energy ef...

Abstract-We consider the problem of planning path and speed of a "data mule" in a sensor network. This problem is encountered in various situations, such as modeling the motion of a data-collecting UAV (Unmanned Aerial Vehicle) in a field of sensors for structural health monitoring. Our specific context here is use of a data mule as an alternative or supplement to multihop forwarding in a sensor network. While a data mule can reduce the energy consumption at each sensor node, it increases the latency from the time the data is generated at a node to the time the base station receives it. In this paper, we introduce the "data mule scheduling" or DMS framework that enables data mule motion planning to minimize the data delivery latency. The DMS framework is general; it can express many previously proposed problem formulations and problem settings related to data mules. We design algorithms for DMS and extend to the more general case of combined data mule and multihop forwarding to enable a flexible trade-off between energy consumption and data delivery latency. Using DMS, we can calculate the optimal way for node-to-node forwarding and data mule motion plan. Our implementation and simulation results using ns2 show nearly monotonic decrease of data delivery latency when each node can use more energy, thus vastly increasing the flexibility in the energy-latency trade-off for sensor network communications.