TECHNOLOGY: DOTNET
DOMAIN: MOBILE COMPUTING
S. No. | IEEE TITLE | ABSTRACT | IEEE YEAR |
1. | An Operations Research Game Approach for Resource and Power Allocation in Cooperative Femtocell Networks | Femtocells are emerging as a key technology to improve coverage and network capacity in indoor environments. When femtocells use different frequency bands than macrocells (i.e., split-spectrum approach), femto-to-femto interference remains the major issue. In particular, congestion cases in which femtocell demands exceed the available resources raise several challenging questions: how much a femtocell can demand? how much it can obtain? and how this shall depends on the interference with its neighbors? Strategic interference management between femtocells via power control and resource allocation mechanisms is needed to avoid performance degradation during congestion cases. In this paper, we model the resource and power allocation problem as an operations research game, where imputations are deduced from cooperative game theory, namely the Shapley value and the Nucleolus, using utility components results of partial optimizations. Based on these evaluations, users’ demands are first rescaled to strategically justified values. Then, a power-level and throughput optimization using the rescaled demands is conducted. The performance of the developed solutions is analyzed and extensive simulation results are presented to illustrate their potential advantages. In particular, we show that the Shapley value solution with power control offers the overall best performance in terms of throughput, fairness, spectrum spatial reuse, and transmit power, with a slightly higher time complexity compared to alternative solutions. | 2015 |
2. | Towards Maximizing Timely Content Delivery in Delay Tolerant Networks | Many applications, such as product promotion advertisement and traffic congestion notification, benefit from opportunistic content exchange in Delay Tolerant Networks (DTNs). An important requirement of such applications is timely delivery. However, the intermittent connectivity of DTNs may significantly delay content exchange, and cannot guarantee timely delivery. The state-of-the-arts capture mobility patterns or social properties of mobile devices. Such solutions do not capture patterns of delivered content in order to optimize content delivery. Without such optimization, the content demanded by a large number of subscribers could follow the same forwarding path as the content by only one subscriber, leading to traffic congestion and packet drop. To address the challenge, in this paper, we develop a solution framework, namely Ameba, for timely delivery. In detail, we first leverage content properties to derive an optimal routing hop count of each content to maximize the number of needed nodes. Next, we develop node utilities to capture interests, capacity and locations of mobile devices. Finally, the distributed forwarding scheme leverages the optimal routing hop count and node utilities to deliver content towards the needed nodes in a timely manner. Illustrative results verify that Ameba achieves comparable delivery ratio as Epidemic but with much lower overhead. | 2015 |
3. | Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection | Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selected features may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than 30 percent energy savings while achieving 96: 7 percent classification accuracy. | 2015 |
4. | On the Energy Efficiency of Device Discovery in Mobile Opportunistic Networks: A Systematic Approach | In this paper, we propose an energy efficient device discovery protocol, eDiscovery, as the first step to bootstrapping opportunistic communications for smartphones, the most popular mobile devices. We chose Bluetooth over WiFi as the underlying wireless technology of device discovery, based on our measurement study of their operational power at different states on smartphones. eDiscovery adaptively changes the duration and interval of Bluetooth inquiry in dynamic environments, by leveraging history information of discovered peers. We implement a prototype of eDiscovery on Nokia N900 smartphones and evaluate its performance in three different environments. To the best of our knowledge, we are the first to conduct extensive performance evaluation of Bluetooth device discovery in the wild. Our experimental results demonstrate that compared with a scheme with constant inquiry duration and interval, eDiscovery can save around 44 percent energy at the expense of discovering only about 21 percent less peers. The results also show that eDiscovery performs better than other existing schemes, by discovering more peers and consuming less energy. We also verify the experimental results through extensive simulation studies in the ns-2 simulator. | 2015 |
5. | ACE: An Accurate and Efficient Multi-Entity Device-Free WLAN Localization System | Device-free (DF) localization in WLANs has been introduced as a value-added service that allows tracking of indoor entities that do not carry any devices. Previous work in DF WLAN localization focused on the tracking of a single entity due to the intractability of the multi-entity tracking problem whose complexity grows exponentially with the number of humans being tracked. In this paper, we introduce ACE: a system that uses a probabilistic energy-minimization framework that combines a conditional random field with a Markov model to capture the temporal and spatial relations between the entities’ poses. A novel cross-calibration technique is introduced to reduce the calibration overhead of multiple entities to linear, regardless of the number of humans being tracked. We design an efficient energy-minimization function that can be mapped to a binary graph-cut problem whose solution has a linear complexity on average and a third order polynomial in the worst case. We further employ clustering on the estimated location candidates to reduce outliers and obtain more accurate tracking in the continuous space. Experimental evaluation in two typical testbeds, with a side-by-side comparison with the state-of-the-art, shows that ACE can achieve a multi-entity tracking accuracy of less than 1.3 m. This corresponds to at least 11.8 percent, and up to 33 percent, enhancement in median distance error over the state-of-the-art DF localization systems. In addition, ACE can estimate the number of entities correctly to within one difference error for 100 percent of the time. This highlights that ACE achieves its goals of having an accurate and efficient multi-entity indoors localization. | 2015 |