Block compressed sensing based distributed resource allocation for M2M communications

Algorithmic metatheorems state that if a problem can be described in a certain logic and the inputs are structured in a certain way, then the problem can be solved with a certain amount of resources. As an example, by Courcelle’s Theorem, all monadic second-order (“in a certain logic”) properties of graphs of bounded tree width (“structured in a certain way”) can be solved in linear time (“with a certain amount of resources”). Such theorems have become valuable tools in algorithmics: if a problem happens to have the right structure and can be described in the right logic, they immediately yield a (typically tight) upper bound on the time complexity of the problem. Perhaps even more importantly, several complex algorithms rely on algorithmic metatheorems internally to solve subproblems, which considerably broadens the range of applications of these theorems. This paper is intended as a gentle introduction to the ideas behind algorithmic metatheorems, especially behind some recent results concerning space and circuit classes, and tries to give a flavor of the range of their applications.

Compressive sampling (CS), or Compressed Sensing , has generated a tremendous amount of excitement in the signal processing community. Compressive sampling, which involves non-traditional samples in the form of randomized projections, can capture most of the salient information in a signal with a relatively small number of samples, often far fewer samples than required using traditional sampling schemes. Adaptive sampling (AS), also called Active Learning , uses information gleaned from previous observations (e.g. , feedback) to focus the sampling process. Theoretical and experimental results have shown that adaptive sampling can dramatically outperform conventional (non-adaptive) sampling schemes. This paper compares the theoretical performance of compressive and adaptive sampling for regression in noisy conditions, and it is shown that for certain classes of piecewise constant signals and high SNR regimes both CS and AS are near optimal. This result is remarkable since it is the first evidence that shows that compressive sampling, which is non-adaptive, cannot be significantly outperformed by any other method (including adaptive sampling procedures), even in the presence of noise. The performance of CS schemes for signal detection is also investigated.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The development of the information sphere and the corresponding increase in its volume and cost is accompanied by the introduction of advanced information technologies in all spheres of public life of the state, which leads to an increase in the frequency of attacks and potential losses from information leakage. As a result, the complexity of protection systems and an increase in their cost. In these conditions, the task to efficiently distribute limited financial resources to protect the information of business entities becomes more and more important and largely determines the level of financial and economic security of the enterprise and the state as a whole. Of particular relevance is the development of issues of optimisation of indicators of the information security system in a dynamic confrontation. Such indicators can be the amount of damage caused from the implementation of information threats, which determines the effectiveness of the protection system, the profit from making investments in information protection, their profitability, and the like. It was established that it is necessary to determine the total amount of protection resources, which minimises the total losses from possible information leakage in conditions of instability. The authors believe that the optimal control mechanism is the distribution of resources between objects that contain a different share of information and are distinguished by natural security, dynamic vulnerability, and the probability of an attack with the allocation of a certain amount of resources. The practical significance of the study lies in determining the optimal moment for the introduction of defence resources – after the direction and intensity of attacks and, accordingly, the possible level of losses was clarified.

Power allocation and measurement matrix design for block CS-based distributed MIMO radars

Sharding technology has been recognized to be a promising solution for blockchain scalability problems in recent years. For safety guarantees in each shard, mainly to prevent the single-shard takeover attack, sharding requires an identity establishment protocol in which participants have to pay a certain amount of resources (i.e., ticket price) to get a node and participate in the network. However, state-of-the-art sharding protocols overlook a non-democratic state of the real-world where every participant has a different amount of resources, termed a non-democratic environment. This oversight raises combined problems of security and scalability due to the design of the identity establishment protocol.In this paper, we examine the effects of the non-democracy of blockchain networks in terms of the security and scalability of blockchain sharding and suggest formulae to quantitatively analyze the trade-off between security and scalability. Moreover, we conduct a numerical analysis by capturing four real-world resource distributions from renowned permissionless cryptocurrency networks. We re-evaluate the well-known sharding protocols through this numerical analysis and present the changed fault tolerance bounds and damage to scalability. The results show that the ticket price plays a leading role in tuning the effect of non-democracy. The main contribution of this paper is the proposal of new metrics for accessing the degree of security and scalability with regard to the ticket price in the identity establishment phase. Our discussion suggests further research on a more delicate ticket price control algorithm when designing a new sharding model for blockchain.KeywordsBlockchain shardingScalabilitySecurity

The heterogeneous cellular network (HCN) is a promising technology to handle the rising number of devices due to their universal presence. This rising popularity HCN based Machine-to-Machine (M2M) communications is opening new opportunities and also bringing forth new system design issues. However, the main challenge of M2M communication is the possibility of huge traffic and significant difference in the nature of M2M traffic than the current commercial traffic for which current cellular network is designed and optimized. In this article, we investigate the uplink resource allocation problem of M2M devices (MDs) in the multiple Femto base station's coverage. We first model the uplink power and sub-carrier allocation in femtocells independently; Based on the cooperative game resource allocation among MDs is analyzed through non-transferable utility game to enhance the data rate performance with minimum utilization of power. Simulation results show that the resource allocation model based on cooperative game is able to provide a fair distribution of data rate compared with non-cooperative and greedy type of MDs.

As one of the three major scenarios of fifth-generation (5G) communication system, massive machine type communications (mMTC) raises more challenges for development of new radio access technology. Unlike the human-to-human communications, the mMTC typically exhibit features such as massive number of devices, small-sized packets and sporadic transmission, which requires novel solutions to satisfy these features. In this paper, we propose a novel grant-free compressive sensing based solution with system level design to jointly conduct the active devices detection, channel estimation and data recovery without knowing the number of active devices. Specifically, in proposed solution, we integrate the sparsity information of channel coefficients and data symbols and exploit the constellation information of modulation and the data diversity of different devices to enhance the performance of receiver. Simulation results demonstrate that the proposed solutions have improve the performance of active device detection, channel estimation, data recovery and the throughput of the system.

We present an automated design flow for minimizing the use of diodes and switches (active devices) in design implementations on a nanofabric based on chemically self-assembled electronic nanotechnology as proposed in Goldstein and Budiu [2001]. Connectivity and logic in the nanofabric are realized using the switch and diode behaviors of molecular devices, unlike very large scale integrated (VLSI) circuits where complementary metal-oxide semiconductor (CMOS) gates are used. Similar to the optimization goal of reducing the number of gates in VLSI designs to minimize area, power dissipation, and delay, decreasing the number of switches and diodes used in the nanofabric can potentially minimize design implementation area and power dissipation, besides reducing the delay and signal drop between latched stages in order to improve performance. An integrated placement, topology selection, and routing approach for design implementation on the nanofabric is proposed. Note that this problem is fundamentally different from CMOS VLSI placement and routing because of the inherent routing-dependent logic realization in our target nanofabric. To the best of our knowledge this is the first reported work on automated integrated placement, topology selection, and routing for diode-based nanofabrics. A practical and scalable simulated annealing-based placement and routing algorithm has been implemented. On average, the integrated placement and routing approach achieves a reduction of 12% in the number of switches and diodes used for MCNC benchmarks, compared to separate placement and routing optimization results. The maximum reduction achieved in the number of active devices using our approach is 24%, and in general, we observed that the bigger the benchmark, the larger the improvement achieved.

Unsourced multiple access abstracts grantless simultaneous communication of a large number of devices (messages) each of which transmits (is transmitted) infrequently. It provides a model for machine-to-machine communication in the Internet of Things, including the special case of radio-frequency identification, as well as neighbour discovery in ad hoc wireless networks. This paper presents a fast algorithm for unsourced multiple access that scales to ${\mathscr{C}}=2^{100}$ (active or non-active) devices (arbitrary $100$ bit messages). The primary building block is multiuser detection of binary chirps, which are simply codewords in the second-order Reed–Muller code. The chirp detection algorithm originally presented by Howard et al. (2008, 42nd Annual Conference on Information Sciences and Systems) is enhanced and integrated into a peeling decoder designed for a patching and slotting framework. In terms of both energy per bit and number of active devices (number of transmitted messages), the proposed algorithm is within a factor of $2$ of state-of-the-art approaches. A significant advantage of our algorithm is its computational efficiency. We prove that the worst-case complexity of the basic chirp reconstruction algorithm is ${\mathscr{O}}[nK(\log _2^2 n + K)]$, where $n$ is the codeword length and $K$ is the number of active users. Crucially, the complexity is sublinear in ${\mathscr{C}}$, which makes the reconstruction computationally feasible—a claim we support by reporting computing times for our algorithm. Our performance and computing time results represent a benchmark against which other practical algorithms can be measured.

Traditionally, any capacity planning problem is modeled with deterministic workloads by considering the peak workload for resource allocation. In the context of businesses using cloud service, cloud provider could allocate resources for peak workload which could lead to under utilization of resource and charging users for unused yet provisioned resources. Hence we came up with a better capacity planning algorithm which could ensure that we plan for peak usage but do not provision for it.In our approach, we modeled the problem as a stochastic optimization problem with the objective of minimizing the number of servers considering two important constraints a) stochastic nature of workloads and b) minimizing the application SLA violations. We implemented the model using genetic algorithm and to address the stochastic nature of work loads, we reserved a free pool of resources in each server by the quantity determined by our algorithm. We evaluated the solution with real sever utilization data from a datacenter seeking consolidation. We did comparative analysis on the number of servers required suggested by our solution vs. peak work loads based solutions for various service levels. Our results illustrate that reserving certain amount of resources in servers for addressing variability of workloads gives better results in terms of lesser number of servers compared to packing resources based on peak workloads for the same service levels.

In this study, we made a simple, low-cost algorithm for tracking eye movements and eye blinks in real-time and non-real-time. Several methods are being used right now. Show parts of the face, like the eyes or the whole face. For this reason, open-source libraries like OpenCV enable high-level programming to implement reliable and accurate detection algorithms like Haar Cascade. Since everything is processed in real-time, payment must be made quickly. Pay attention to how hardware, like a computer, can only use a certain amount of resources (processing power). The system has been proven to work by tests with 15 people of different ages and backgrounds. These tests are done to see how the user and the device work together and ensure everything works correctly. The In the tests done, the system worked between 80% and 100% of the time. The results showed that Haar Cascade had a significant effect by Detection of faces in 100% of cases, while the eyes and pupil where they overlap (light and shade) is less effective. In addition to looking at how well the Through these activities, the demo application also showed that user restrictions shouldn't stop people from enjoying and using a certain type of technology. The program was written in C++, and the OpenCV library makes it work on Windows. This system has many uses in the real world and science. By looking at the data from this algorithm from afar, for example, it can tell if someone has an eye disease or is tired. It can also help people who have physical or mental problems

Shared resource pools are facilities featuring a certain amount of resources which can be used by different applications. For managing resources in such pools, the demand of each application can be used. Such a demand, however, is driven by the workload, which varies over time. For that reason, adaptive approaches have been proposed for the management of shared resources pools. Whereas a number of solutions exist in this context, they are either not truly decentralized or do not apply to the problem we are dealing with. In this paper, we then present Darma, an approach for managing shared resource pools in a truly decentralized, adaptive, and optimal way.

Big but also small and medium-sized enterprises (SMEs) are driven by the idea of entering foreign markets. Globalisation plays an important role for many companies. Based on its size and its financial strength, each company has access to a certain amount of resources. SMEs have limited resources. This disadvantage influences, inter alia, internationalisation strategy and process of SMEs. Social capital can compensate disadvantages, and plays a significant role for SMEs and their strategy. This study focuses on the whole internationalisation to clarify the influence of social capital including the generation and use of knowledge and experience for further market entries. Theoretical fundamentals are network and social capital theory, and multiple case-study method. These approaches help to explore the influence of social capital on the internationalisation process and strategy. The results of this study present the significance of social capital for the internationalisation and the limited awareness of SMEs' network resources.

The earlier parts of this book suggest the areas which require empirical illumination. Chapter 1 presented a general model of technological choice: each technique is associated with a vector of characteristics; the set of techniques available depends on the historical development of technology, and specifically the economic/social conditions of the economy for which the techniques were originally developed; each decision maker has certain objectives, a certain amount of knowledge about the technological possibilities, faces certain restraints, and controls a certain amount of resources. The actual choice made then depends on the interaction between decision makers and technological possibilities, given the various objectives and restraints. Ideally, empirical studies should try and illuminate this complex process — revealing for example how the historical development of the industry affects the technology available; whether and how one set of decision makers, e.g. multinational firms, make a different choice from another, e.g. local publicly owned firms; and why — e.g. because objectives differ, or because access to resources differ, or because markets differ. Chapter 4 presented a more specific empirical question: that is, the question of the existence and relative efficiency of an appropriate technology, and the sort of socio-economic changes that would be needed to get such a technology — if it exists — into use. This question is in a way a sub-question of the general model of technological choice in Chapter 1.KeywordsInvestment CostCapacity UtilisationLocal FirmTechnological ChoiceSurplus GenerationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Spectrum-efficient mode selection and fair resource allocation for D2D-enabled uplink/downlink MC-NOMA networks