It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Wireless sensor networks for healthcare: A survey

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

The development of the wireless body area sensor network (BASN) is imperative for modern telemedicine and m-health, but security remains a formidable challenge yet to be resolved. As nodes of BASN are expected to be interconnected on or in the human body, the body itself can form an inherently secure communication pathway that is unavailable to all other kinds of wireless networks. This article explores the use of this conduit in the security mechanism of BASN; that is, by a biometrics approach that uses an intrinsic characteristic of the human body as the authentication identity or the means of securing the distribution of a cipher key to secure inter-BASN communications. The method was tested on 99 subjects with 838 segments of simultaneous recordings of electrocardiogram and photoplethysmogram. By using the interpulse interval (IPI) as the biometric trait, the system achieved a minimum half total error rate of 2.58 percent when the IPIs measured from signals, which were sampled at 1000 Hz, were coded into 128-bit binary sequences. The study opens up a few key issues for future investigation, including compensation schemes for the asynchrony of different channels, coding schemes, and other suitable biometric traits.

A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks

In this paper, we propose a distributed joint computation offloading and resource allocation optimization (JCORAO) scheme in heterogeneous networks with mobile edge computing. An optimization problem is formulated to provide the optimal computation offloading strategy policy, uplink subchannel allocation, uplink transmission power allocation, and computation resource scheduling. The optimization problem is decomposed into two sub-problems due to the NP-hard property. In order to analyze the offloading strategy, a sub-algorithm named distributed potential game is built. The existence of Nash equilibrium is proved. To jointly allocate uplink subchannel, uplink transmission power, and computation resource for the offloading mobile terminals, a sub-algorithm named cloud and wireless resource allocation algorithm is designed. The solutions for subchannel allocation consist of uniform zero frequency reuse method without interference and fractional frequency reuse method based on Hungarian and graph coloring with interference. A distributed JCORAO scheme is proposed to solve the optimization problem by the mutual iteration of the two sub-algorithms. Simulation results show that the distributed JCORAO scheme can effectively decrease the energy consumption and task completion time with lower complexity.

Joint Computation Offloading and Resource Allocation Optimization in Heterogeneous Networks With Mobile Edge Computing

With the evolution of m-Health, an increasing number of biomedical sensors will be worn on or implanted in an individual in the future for the monitoring, diagnosis, and treatment of diseases. For the optimization of resources, it is therefore necessary to investigate how to interconnect these sensors in a wireless body area network, wherein security of private data transmission is always a major concern. This paper proposes a novel solution to tackle the problem of entity authentication in body area sensor network (BASN) for m-Health. Physiological signals detected by biomedical sensors have dual functions: (1) for a specific medical application, and (2) for sensors in the same BASN to recognize each other by biometrics. A feasibility study of proposed entity authentication scheme was carried out on 12 healthy individuals, each with 2 channels of photoplethysmogram (PPG) captured simultaneously at different parts of the body. The beat-to-beat heartbeat interval is used as a biometric characteristic to generate identity of the individual. The results of statistical analysis suggest that it is a possible biometric feature for the entity authentication of BASN

Physiological Signal Based Entity Authentication for Body Area Sensor Networks and Mobile Healthcare Systems

Vehicular Ad-hoc Network (VANET) is a new application of Mobile Ad-hoc Network (MANET) in the field of Inter-vehicle communication. As the high mobility of vehicles, some traditional MANET routing protocols may not fit the VANET. In this paper, we propose a cluster-based directional routing protocol (CBDRP) for highway scenarios, in which the header of a cluster selects another header according to the moving direction of vehicle to forward packets. Simulation results shows the CBDRP can solve the problem of link stability in VANET, realizing reliable and rapid data transmission.

A cluster-based directional routing protocol in VANET

The authors present a new method for data gathering that maximizes lifetime for wireless sensor networks. It involves three parts. First, nodes organize themselves into several static clusters by the Hausdorff clustering algorithm based on node locations, communication efficiency, and network connectivity. Second, clusters are formed only once, and the role of the cluster head is optimally scheduled among the cluster members. We formulate the maximum lifetime cluster-head scheduling as an integer-programming problem and propose a greedy algorithm for its solution. Third, after cluster heads are selected, they form a backbone network to periodically collect, aggregate, and forward data to the base station using minimum energy (cost) routing. This method can significantly lengthen the network lifetime when compared with other known methods.

/pdf/hausdorff-clustering-and-minimum-energy-routing-for-wireless-2i61u3oea5.pdf

Hausdorff Clustering and Minimum Energy Routing for Wireless Sensor Networks

We present a new method for data gathering that maximizes lifetime for wireless sensor networks. It involves three parts. First, nodes organize themselves into several static clusters by the Hausdorff clustering algorithm based on location, communication efficiency and network connectivity. Second, clusters are formed only once but the role of cluster-head is optimally scheduled among the cluster members. We formulate the cluster-head scheduling that maximizes the network lifetime as an integer programming problem and propose a greedy algorithm for its solution. Third, after cluster-heads are selected, they form a backbone network to periodically collect, aggregate, and forward data to the base station, where a minimum energy (cost) routing is used. Comparing with other known methods, significant lifetime extension is obtained with the use of this method.

/pdf/hausdorff-clustering-and-minimum-energy-routing-for-wireless-1uermuaa2e.pdf

Lianfeng Shen

Papers

Joint Computation Offloading and Resource Allocation Optimization in Heterogeneous Networks With Mobile Edge Computing

Physiological Signal Based Entity Authentication for Body Area Sensor Networks and Mobile Healthcare Systems

A cluster-based directional routing protocol in VANET

Hausdorff Clustering and Minimum Energy Routing for Wireless Sensor Networks

Hausdorff Clustering and Minimum Energy Routing for Wireless Sensor Networks