A Feasibility Study of RIP Using 2.4 GHz 802.15.4 Radios

This paper contains a feasibility study of Radio Interferometric Positioning (RIP) implemented on a widely used 2.4 GHz radio (CC2430). RIP is a relatively new localization technique that uses signal strength measurements. Although RIP outperforms other RSS-based localization techniques, it imposes a set of unique requirements on the used radios. Therefore, it is not surprising that all existing RIP implementations use the same radio (CC1000), which operates below the 1 GHz range. This paper analyzes to what extent the CC2430 complies with these requirements. This analysis shows that the CC2430 platform introduces large and dynamic sources of errors. Measurements with a CC2430 test bed in a line-of-sight indoor environment verify this. The measurements indicate that the existing RIP algorithm cannot cope with these types of errors, and will incur a relatively low accuracy of 3.1 meter. Based on these results, we made an initial implementation of a new algorithm, which can cope with these errors, and decreases this positioning error by a factor of two to 1.5 meter accuracy

High Dimensional Random Walks and Colorful Expansion

Random walks on bounded degree expander graphs have numerous applications, both in theoretical and practical computational problems. A key property of these walks is that they converge rapidly to their stationary distribution. In this work we {\em define high order random walks}: These are generalizations of random walks on graphs to high dimensional simplicial complexes, which are the high dimensional analogues of graphs. A simplicial complex of dimension $d$ has vertices, edges, triangles, pyramids, up to $d$-dimensional cells. For any $0 \leq i < d$, a high order random walk on dimension $i$ moves between neighboring $i$-faces (e.g., edges) of the complex, where two $i$-faces are considered neighbors if they share a common $(i+1)$-face (e.g., a triangle). The case of $i=0$ recovers the well studied random walk on graphs. We provide a {\em local-to-global criterion} on a complex which implies {\em rapid convergence of all high order random walks} on it. Specifically, we prove that if the $1$-dimensional skeletons of all the links of a complex are spectral expanders, then for {\em all} $0 \le i < d$ the high order random walk on dimension $i$ converges rapidly to its stationary distribution. We derive our result through a new notion of high dimensional combinatorial expansion of complexes which we term {\em colorful expansion}. This notion is a natural generalization of combinatorial expansion of graphs and is strongly related to the convergence rate of the high order random walks. We further show an explicit family of {\em bounded degree} complexes which satisfy this criterion. Specifically, we show that Ramanujan complexes meet this criterion, and thus form an explicit family of bounded degree high dimensional simplicial complexes in which all of the high order random walks converge rapidly to their stationary distribution.Comment: 27 page

Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling

Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow $46\%$ less number of samples for accurate signal reconstruction and achieve up to $57\%$ smaller signal reconstruction error under the same noise condition.Comment: 9 pages, IEEE MASS 201

Energy-Efficient selective activation in Femtocell Networks

Provisioning the capacity of wireless networks is difficult when peak load is significantly higher than average load, for example, in public spaces like airports or train stations. Service providers can use femtocells and small cells to increase local capacity, but deploying enough femtocells to serve peak loads requires a large number of femtocells that will remain idle most of the time, which wastes a significant amount of power. To reduce the energy consumption of over-provisioned femtocell networks, we formulate a femtocell selective activation problem, which we formalize as an integer nonlinear optimization problem. Then we introduce GREENFEMTO, a distributed femtocell selective activation algorithm that deactivates idle femtocells to save power and activates them on-the-fly as the number of users increases. We prove that GREENFEMTO converges to a locally Pareto optimal solution and demonstrate its performance using extensive simulations of an LTE wireless system. Overall, we find that GREENFEMTO requires up to 55% fewer femtocells to serve a given user load, relative to an existing femtocell power-saving procedure, and comes within 15% of a globally optimal solution

BANZKP: a Secure Authentication Scheme Using Zero Knowledge Proof for WBANs

-Wireless body area network(WBAN) has shown great potential in improving healthcare quality not only for patients but also for medical staff. However, security and privacy are still an important issue in WBANs especially in multi-hop architectures. In this paper, we propose and present the design and the evaluation of a secure lightweight and energy efficient authentication scheme BANZKP based on an efficient cryptographic protocol, Zero Knowledge Proof (ZKP) and a commitment scheme. ZKP is used to confirm the identify of the sensor nodes, with small computational requirement, which is favorable for body sensors given their limited resources, while the commitment scheme is used to deal with replay attacks and hence the injection attacks by committing a message and revealing the key later. Our scheme reduces the memory requirement by 56.13 % compared to TinyZKP [13], the comparable alternative so far for Body Area Networks, and uses 10 % less energy

Implementing and Evaluating Jukebox Schedulers Using JukeTools

Scheduling jukebox resources is important to build efficient and flexible hierarchical storage systems. JukeTools is a toolbox that helps in the complex tasks of implementing and evaluating jukebox schedulers. It allows the fast development of jukebox schedulers. The schedulers can be tested in numerous environments, both real and simulated types. JukeTools helps the developer to easily detect errors in the schedules. Analyzer tools create detailed reports on the behavior and performance of any of the scheduler, and provide comparisons between different schedulers. This paper describes the functionality offered by JukeTools, with special emphasis on how the toolbox can be used to develop jukebox schedulers

Local-To-Global Agreement Expansion via the Variance Method

Agreement expansion is concerned with set systems for which local assignments to the sets with almost perfect pairwise consistency (i.e., most overlapping pairs of sets agree on their intersections) implies the existence of a global assignment to the ground set (from which the sets are defined) that agrees with most of the local assignments. It is currently known that if a set system forms a two-sided or a partite high dimensional expander then agreement expansion is implied. However, it was not known whether agreement expansion can be implied for one-sided high dimensional expanders. In this work we show that agreement expansion can be deduced for one-sided high dimensional expanders assuming that all the vertices\u27 links (i.e., the neighborhoods of the vertices) are agreement expanders. Thus, for one-sided high dimensional expander, an agreement expansion of the large complicated complex can be deduced from agreement expansion of its small simple links. Using our result, we settle the open question whether the well studied Ramanujan complexes are agreement expanders. These complexes are neither partite nor two-sided high dimensional expanders. However, they are one-sided high dimensional expanders for which their links are partite and hence are agreement expanders. Thus, our result implies that Ramanujan complexes are agreement expanders, answering affirmatively the aforementioned open question. The local-to-global agreement expansion that we prove is based on the variance method that we develop. We show that for a high dimensional expander, if we define a function on its top faces and consider its local averages over the links then the variance of these local averages is much smaller than the global variance of the original function. This decreasing in the variance enables us to construct one global agreement function that ties together all local agreement functions