The migration of application logic to the client side of modern web applications and the use of JavaScript as the main language for client-side development have made memory leaks in JavaScript an issue for web applications. Client-side web applications communicate with the server asynchronously, remaining on the same web page during their lifetime. Thus, even minor memory leaks can eventually lead to excessive memory usage, negatively affecting user-perceived response time and possibly causing page crashes. To detect memory leaks and guide developers in fixing the leaks quickly and easily, this paper introduces LeakSpot, a tool that creates a run-time heap model by modifying the application code in a browser-agnostic way to record object allocations, accesses, and references created to objects. LeakSpot reports those allocation sites causing the leaks instead of all the leaky allocation sites. It also identifies the locations in the code where leaked objects are accumulated, for example, the locations where a reference from a data structure is created but forgotten to be removed by the developer. To facilitate debugging and fixing the leaks, for every leaked object, LeakSpot reports all the locations in the code that create a reference to the object. To confirm usefulness and efficacy of LeakSpot experimentally, we have used LeakSpot to find and fix four memory leaks in a JavaScript benchmark suite and in open-source web applications. LeakSpot is also shown to be effective in pointing out the potential causes of three leaks in large and popular web applications. Copyright © 2016 John Wiley & Sons, Ltd.

A fundamental problem in wireless sensor networks is localization -- the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms -- they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.

Sensor networks are expected to revolutionize information gathering, processing and dissemination in many diverse environments. In this paper, we address a fundamental problem in designing sensor networks: localization, or determining the locations of nodes. We assume that a small fraction of the sensor nodes (called seeds) know their locations. We propose an algorithm that enables other nodes to estimate their locations by exchanging information between nodes and seeds. Unlike most existing work, in our algorithm, a node uses the location information of all its neighbors, not just the seed nodes. Unlike most existing algorithms our algorithm works for both static and mobile sensor networks. Using simulation experiments, we demonstrate that our algorithm significantly outperforms comparable existing algorithms like DV-hop [1] and MCL [2]