A security architecture for personal networks

Abstract Personal Network (PN) is a new concept utilizing pervasive computing to meet the needs of the user. As PNs edge closer towards reality, security becomes an important concern since any vulnerability in the system will limit its practical use. In this paper we introduce a security architecture designed for PNs. Our aim is to use secure but lightweight mechanisms suitable for resource constrained devices and wireless communication. We support pair-wise keys for secure cluster formation and use group keys for securing intra-cluster communication. In order to analyze the performance of our proposed mechanisms, we carry out simulations using ns-2. The results show that our mechanisms have a low overhead in terms of delay and energy consumption

Comments on "Minimum-Latency Transport Protocols with Modulo-N Incarnation Numbers"

The authors comment on a class of minimum-latency transport protocols that have been analyzed by Shankar and Lee (see ibid., vol.3, no.3, p.255, 1995). The protocols use unique incarnation numbers and caching schemes to reduce the latency of connection setup whenever possible. They discuss three modifications to the protocol. (1) A modification to the opening procedure which eliminates some constraints for the correctness of the protocol. (2) A modification which allows data messages in the opening state of the client to be sent. This reduces the latency in some situations for the price of stricter constraints for correctness. (3) An alternate way of closing connections. Apart from these modifications, they also show that the proofs can be refined to get somewhat less restrictive constraints for the correctness of the protoco

Rate-optimal scheduling of recursive DSP algorithms based on the scheduling-range chart

A method for rate-optimal scheduling of recursive DSP algorithms is presented. The approach is based on the determination of the scheduling window of each operation and the construction of a scheduling-range chart. The information in the chart is used during scheduling to optimize some quality criteria (number of hardware resources, latency, register life time) at the same time that a rate-optimal solution is guaranteed. An algorithm based on this approach is introduced. It can schedule cyclic as well as acyclic data-flow graphs. The algorithm is powerful enough to solve optimally some problems for which other proposed methods fai

Maximum-throughput scheduling with limited resources for iterative data-flow graphs by means of the scheduling-range chart

An algorithm based on an alternative scheduling approach for iterative acyclic and cyclid DFGs (data-flow graphs) with limited resources that exploits inter- and intra-iteration parallelism is presented. The method is based on guiding the scheduling algorithm with the information supplied by a scheduling-range chart. This scheduling range is relative to a reference operation and can be finite or infinite. The information in the scheduling-range chart is used during scheduling in order to optimize the sampling period. For cases where the precedence constraints do not allow a schedule in the originally selected optimal sampling period, the algorithm provides an adjustment procedure, thus always guaranteeing a solution. The delay of the processor-assignment phase increases the efficiency of the algorithm when the operations have processing times different from the unity and are nonpreemptiv

Power Allocation in Cell-Free Massive MIMO: A Deep Learning Method

Massive multiple-input multiple-output (MIMO) is a key technology in 5G. It enables multiple users to be served in the same time-frequency block through precoding or beamforming techniques, thus increasing capacity, reliability and energy efficiency. A key issue in massive MIMO is the allocation of power to the individual antennas, in order to achieve a specific objective, e.g., the maximization of the minimum capacity guaranteed to each user. This is a nondeterministic polynomial (NP)-hard problem that needs to be solved in a timely manner since the state of the channels evolves in time and the power allocation should stay in tune with this state. Although several heuristics have been proposed to solve this problem, these entail a considerable time-complexity. As a result, with the present methods, it cannot be guaranteed that power allocation happens in time. To solve this problem, we propose a deep neural network (DNN). A DNN has a low time complexity, but requires an extensive, offline, training process before it becomes operational. The DNN we propose is the combination of two convolutional layers and four fully connected layers. It takes as input the long-term fading information and it outputs the power for each antenna element to each user. We limit ourselves to the case of time-division duplex (TDD) based sub-6GHz networks. Numerical results show that, our DNN-based method approximates very closely the results of a commonly used heuristic based on the bisection algorithm

Distributed mmWave Massive MIMO: A Performance Comparison with a Centralized Architecture for Various Degrees of Hybridization

mmWave massive MIMO (multiple-input multipleoutput) is envisioned to offer a considerable capacity improvement with respect to sub-6GHz systems. However, it suffers much more from path loss and shadowing. Moreover, the present hardware constraints limit the number of radiofrequency (RF) chains, which, in turn, significantly limits the performance of the system achievable with full digital beamforming. To mitigate these performance limiting factors, we propose to geographically distribute the antennas over the coverage area and cluster them into a number of remote antenna arrays (RAA). This leads to a shorter average distance between antennas and user equipment (UEs), more spatial diversity against shadowing and the opportunity to overcome the limitations on the number of RF chains. We analyze and compare, for a mixed-office environment and a specific geographical distribution of the RAAs, the performance of a centralized architecture and a distributed one with varying numbers of RF chains, encompassing hybrid beamforming and full digital beamforming. We assume low-complexity zeroforcing (ZF) beamforming on the downlink and max-min power allocation. Using Monte Carlo simulation, we obtain the empirical CDF of the per-UE spectral efficiency (SE). The results show that: (a) The distributed architecture has better performance than the centralized one in terms of per-UE SE; (b) When the number of RF chains is no less than twice the number of UEs, hybrid beamforming achieves the same performance as full digital beamforming in terms of per-UE SE