The Internet consists of economically selfish players in terms of access/transit connection and content distribution. Such selfish behaviors often lead to techno-economic inefficiencies, such as unstable peering and revenue imbalance. Recent research results suggest that cooperation-based fair revenue sharing, i.e., multi-level Internet service provider (ISP) settlements, can be a candidate solution to avoid unfair revenue share. However, it has been under-explored whether selfish ISPs actually cooperate or not (often referred to as the stability of coalition), because they may partially cooperate or even do not cooperate, depending on how much revenue is distributed to each individual ISP. In this paper, we study this stability of coalition in the Internet, where our aim is to investigate the conditions under which ISPs cooperate under different regimes on the traffic demand and network bandwidth. We first consider the under-demanded regime, i.e., network bandwidth exceeds traffic demand, where revenue sharing based on Shapley value leads ISPs to entirely cooperate, i.e., stability of the grand coalition. Next, we consider the over-demanded regime, i.e., traffic demand exceeds network bandwidth, where there may exist some ISPs who deviate from the grand coalition. In particular, this deviation depends on how users' traffic is handled inside the network, for which we consider three traffic scheduling policies having various degrees of content-value preference. We analytically compare those three scheduling policies in terms of network neutrality, and stability of cooperation that provides useful implications on when and how multi-level ISP settlements help and how the Internet should be operated for stable peering and revenue balance among ISPs.

IoT technology is considered an essential imperative for Smart Grids (SGs). However, IoT devices have inherently limited responsiveness that may not be sufficient for a time critical SG with stringent demands on communication delay. In practice, it remains an outstanding problem to combine IoT technology with existing grids. To facilitate deployment of IoT-based grids in domestic environments, we propose IoT-grid, a programmable, small-scale, direct current (DC) grid, that can be easily implemented with low-power hardware with limited processing capacity. The proposed grid adopts relatively cheap DC-DC converters which not only provide high conversion efficiency but also accommodate existing small-scale DC power systems (e.g. solar panels). We then explore the communication aspects of IoT-grid, namely, control and monitoring functions. We observe that processing delays of IoT devices have large impact on IoT-grid, which cause a chain of control commands to take considerable longer time as the number of commands increases. To mitigate this problem, we propose a mechanism based on sending burst commands with scheduled responses. Our experimental results show that, in the presence of processing delays, this method can significantly reduce the overall response time.

An essential condition precedent to the success of mobile applications based on Wi-Fi (e.g., iCloud) is an energy-efficient Wi-Fi sensing. Clearly, a good Wi-Fi sensing policy should factor in both inter-access point (AP) arrival time (IAT) and contact duration time (CDT) distributions of each individual. However, prior work focuses on limited cases of those two distributions (e.g., exponential) or proposes heuristic approaches such as Additive Increase (AI). In this paper, we first formulate a generalized functional optimization problem on Wi-Fi sensing under general inter-AP and contact duration distributions and investigate how each individual should sense Wi-Fi APs to strike a good balance between energy efficiency and performance, which is in turn intricately linked with users mobility patterns. We then derive a generic optimal condition that sheds insights into the aging property, underpinning energy-aware Wi-Fi sensing polices. In harnessing our analytical findings and the implications thereof, we develop a new sensing algorithm, called Wi-Fi Sensing with AGing (WiSAG), and demonstrate that WiSAG outperforms the existing sensing algorithms up to 37% through extensive trace-driven simulations for which real mobility traces gathered from hundreds of smartphones is used.

This paper discovers fundamental principles of the backoff process that governs the performance of IEEE 802.11. A simplistic principle founded upon regular variation theory is that the backoff time has a truncated Pareto-type tail distribution with an exponent of (log y)/ log m (m is the multiplicative factor and γ is the collision probability). This reveals that the per-node backoff process is heavy-tailed in the strict sense for γ > 1/m2, and paves the way for the following unifying result. The state-of-the-art theory on the superposition of the heavy-tailed processes is applied to establish a dichotomy exhibited by the aggregate backoff process, putting emphasis on the importance of time-scales on which we view the backoff processes. While the aggregation on normal time-scales leads to a Poisson process, it is approximated by a new limiting process possessing long-range dependence (LRD) on coarse time-scales. This dichotomy turns out to be instrumental in formulating short-term fairness, extending existing formulas to arbitrary population, and to elucidate the absence of LRD in practical situations. A refined wavelet analysis is conducted to strengthen this argument.

This paper studies an incentive structure for cooperation and its stability in peer-assisted services when there exist multiple content providers, using a coalition game-theoretic approach. We first consider a generalized coalition structure consisting of multiple providers with many assisting peers, where peers assist providers to reduce the operational cost in content distribution. To distribute the profit from cost reduction to players (i.e, providers and peers), we then establish a generalized formula for individual payoffs when a “Shapley-like” payoff mechanism is adopted. We show that the grand coalition is unstable, even when the operational cost functions are concave, which is in sharp contrast to the recently studied case of a single provider where the grand coalition is stable. We also show that irrespective of stability of the grand coalition, there always exist coalition structures that are not convergent to the grand coalition under a dynamic among coalition structures. Our results give us an incontestable fact that a provider does not tend to cooperate with other providers in peer-assisted services and is separated from them. Three facets of the noncooperative (selfish) providers are illustrated: 1) underpaid peers; 2) service monopoly; and 3) oscillatory coalition structure. Lastly, we propose a stable payoff mechanism that improves fairness of profit sharing by regulating the selfishness of the players as well as grants the content providers a limited right of realistic bargaining. Our study opens many new questions such as realistic and efficient incentive structures and the tradeoffs between fairness and individual providers' competition in peer-assisted services.

An essential condition precedent to the success of mobile applications based on Wi-Fi (e.g., iCloud) is an energy-efficient Wi-Fi sensing. From a user's perspective, a good WiFi sensing policy should depend on both inter-AP arrival and contact duration time distributions. Prior work focuses on limited cases of those two distributions (e.g., exponential) or introduces heuristic approaches such as AI (Additive Increase). In this paper, we formulate a functional optimization problem on Wi-Fi sensing under general inter-AP and contact duration distributions, and propose how each user should sense Wi-Fi APs to strike a balance between energy efficiency and performance, depending on the users' mobility pattern. To that end, we derive an optimal condition which sheds insights into the aging property, the key feature required by efficient Wi-Fi sensing polices. Guided by the analytical studies and the implications, we develop a new sensing algorithm, called WiSAG (Wi-Fi Sensing with AGing), which is demonstrated to outperform the existing sensing algorithms up to 34% through extensive trace-driven simulations using the real mobility traces gathered from smartphones.

The Internet consists of economically selfish players in terms of access/transit connection, content distribution, and users. Such selfish behaviors often lead to techno-economic inefficiencies such as unstable peering and revenue imbalance. Recent research results suggest that cooperation in revenue sharing (thus multi-level ISP settlements) can be a candidate solution for the problem of unfair revenue share. However, it is unclear whether providers are willing to behave cooperatively. In this paper, we study the interaction between how content-oriented traffic scheduling at the edge is and how stable the intended cooperation is. We consider three traffic scheduling policies having various degrees of content-value preference, compare them in terms of implementation complexity, network neutrality, and stability of cooperation, and present interesting trade-offs among them.

Performance evaluation of the 802.11 MAC protocol is classically based on the decoupling assumption, which hypothesizes that the backoff processes at different nodes are independent. This decoupling assumption results from mean field convergence and is generally true in transient regime in the asymptotic sense (when the number of wireless nodes tends to infinity), but, contrary to widespread belief, may not necessarily hold in stationary regime. The issue is often related with the existence and uniqueness of a solution to a fixed point equation; however, it was also recently shown that this condition is not sufficient; in contrast, a sufficient condition is a global stability property of the associated ordinary differential equation. In this paper, we give a simple condition that establishes the asymptotic validity of the decoupling assumption for the homogeneous case (all nodes have the same parameters). We also discuss the heterogeneous and the differentiated service cases and formulate a new ordinary differential equation. We show that the uniqueness of a solution to the associated fixed point equation is not sufficient; we exhibit one case where the fixed point equation has a unique solution but the decoupling assumption is not valid in the asymptotic sense in stationary regime.

CDMA2000, WCDMA and WiMAX are three widely used 3G technologies. Since they share the same goal, which is to provide broader coverage and higher throughput in 3G networks, an impartial comparison of their performance is indispensable. However, they are based on different design principles and methodologies, which make the comparison challenging. This paper presents a comparative study of these technologies, with focus on system spectral efficiency and stability in 3G networks. Specifically, the paper presents a framework for the comparison based on the common set of configurations adopted by these technologies, which include channel models, system parameters and key algorithms. Through extensive simulations, the system spectral efficiency and stability of CDMA2000 1x EV-DO Rev.A, WCDMA HSDPA/HSUPA and mobile WiMAX are compared. It is found that while WiMAX can provide highest throughput, the two CDMA-based technologies achieve higher system spectral efficiency, especially on the downlink. Regarding system stability, it is observed that CDMA2000 1x EV-DO Rev.A can operate under higher interference levels than WCDMA HSDPA/HSUPA and mobile WiMAX. In addition, the comparison on system spectral efficiency between CDMA2000, WCDMA and WiMAX is also conducted when relevant enhanced technologies, i.e., MIMO and interference cancellation, are adopted. We believe that our work will serve as a cornerstone for a fair comparison between the various technologies for prospective 3G networks.

We present a unified analytical framework that maximizes generalized utilities of a wireless network by network-wide opportunistic scheduling and power control. That is, base stations in the network jointly decide mobile stations to be served at the same time as the transmission powers of base stations are coordinated to mitigate the mutually interfering effect. Although the maximization at the first glance appears to be a mixed, twofold and nonlinear optimization requiring excessive computational complexity, we show that the maximization can be transformed into a pure binary optimization with much lower complexity. To be exact, it is proven that binary power control of base stations is necessary and sufficient for maximizing the network-wide utilities under a physical layer regime where the channel capacity is linear in the signal-to-interference-noise ratio. To further reduce the complexity of the problem, a distributed heuristic algorithm is proposed that performs much better than existing opportunistic algorithms. Through extensive simulations, it becomes clear that network-wide opportunistic scheduling and power control is most suitable for fairness-oriented networks and under loaded networks. We believe that our work will serve as a cornerstone for network-wide scheduling approaches from theoretical and practical standpoints.

We present a unified analytical framework that maximizes generalized utilities of a wireless network by network-wide opportunistic scheduling and power control. That is, base stations in the network jointly decide mobile stations to be served at the same time as the transmission powers of base stations are coordinated to mitigate the mutually interfering effect. Although the maximization at the first glance appears to be a mixed, twofold and nonlinear optimization requiring excessive computational complexity, we show that the maximization can be transformed into a pure binary optimization with much lower complexity. To be exact, it is proven that binary power control of base stations is necessary and sufficient for maximizing the network-wide utilities under a physical layer regime where the channel capacity is linear in the signal-to-interference-noise ratio. To further reduce the complexity of the problem, a distributed heuristic algorithm is proposed that performs much better than existing opportunistic algorithms. Through extensive simulations, it becomes clear that network-wide opportunistic scheduling and power control is most suitable for fairness-oriented networks and underloaded networks.

We present a network architecture for the distributed utility max-min flow control of elastic and nonelastic flows where utility values of users (rather than data rates of users) are enforced to achieve max-min fairness. The proposed link algorithm converges to utility max-min fair bandwidth allocation in the presence of round-trip delays without using the information of users' utility functions. To show that the proposed algorithm can be stabilized not locally but globally, we found that the use of nonlinear control theory is inevitable. Even though we use a distributed flow-control algorithm, it is shown that any kind of utility function can be used as long as the minimum slopes of the functions are greater than a certain positive value. Though our analysis is limited to the single-bottleneck and homogeneous-delay case, we believe that the proposed algorithm is the first to achieve utility max-min fairness with guaranteed stability in a distributed manner

We propose an analytical framework that optimizes the performance of various Hybrid Automatic Repeat reQuest (HARQ) schemes in wireless packet data systems. The optimization framework lends itself to maximizing the user throughput without assuming a specific HARQ scheme, a target packet error rate, and a mapping of signal-to-interference-noise ratios (SINR) into modulation and coding scheme (MCS) sets. Thus, a user selects an optimal amount of redundancy bits used for retransmissions, an optimal packet error rate, and an optimal SINR-MCS mapping such that the user throughput would be maximized. We show that there exists an optimal redundancy block size of incremental redundancy (IR) scheme when the number of retransmissions is limited, while as small as possible redundancy size is optimal when the number of retransmissions is unlimited. Our analytical results are verified through extensive simulations of the IEEE 802.16e system.

We present a network architecture for the distributed utility max-min flow control of elastic and non-elastic flows where utility values of users (rather than data rates of users) are enforced to achieve max-min fairness. We provide a distributed link algorithm that does not use the information of users' utility functions. To show that the proposed algorithm can be stabilized not locally but globally, we found that the use of nonlinear control theory is inevitable. Even though we use a distributed flow control algorithm, it is shown that any kind of utility function can be used as long as the minimum slopes of the functions are greater than a certain positive value. We believe that the proposed algorithm is the first to achieve utility max-min fairness with guaranteed stability in a distributed manner.

This paper describes an analytical framework for the weighted max-min flow control of elastic flows in packet networks using PID and PII/sup 2/ controllers when flows experience heterogeneous round-trip delays (HRTD). Our algorithms are scalable in that routers do not need to store any per-flow information and they use a simple first come first serve (FCFS) discipline, and stable in that the stability is proven rigorously when there are flows with HRTD. We first suggest two closed-loop system models that approximate our flow control algorithms in the continuous-time domain where the purpose of the first algorithm is to achieve the target queue length and that of the second is to achieve the target utilization. The slow convergence of source rates traversing routers with empty buffers, which is inherent in many flow control algorithms, can be resolved by the second algorithm. Based on these models, we find the conditions for controller gains that stabilize the closed-loop systems when round-trip delays are equal and extend this result to the case of HRTD with the help of the zero exclusion theorem.

We present a distributed algorithm to compute the bandwidth max-min fair rates in a multi-rate multicast network. The significance of the algorithm, compared to previous algorithms (Sakar, S. and Tassiulas L., 1999, 2000; Kar, K. et al., 2001), is that it is more scalable (it does not require each link to maintain the saturation status of all sessions and virtual sessions travelling through it) it is more stable (it converges asymptotically to the desired equilibrium, satisfying the minimum plus max-min fairness, even in the presence of heterogeneous round-trip delays) and it has explicit link buffer control (the buffer occupancy of every bottlenecked link in the network asymptotically converges to the pre-defined value). In addition, we propose an efficient feedback consolidation algorithm which is computationally simpler than its hard-synchronization based counterpart and eliminates unnecessary consolidation delay by preventing it from awaiting backward control packets (BCPs) that do not directly contribute to the session rate. Through simulations, we verify the performance of the proposed multi-rate multicast flow control scheme based on these two algorithms.

One of main issues in 1/spl times/EV-DV systems is how to support and satisfy various QoS requirements of data services and voice service simultaneously. Another main issue in 1/spl times/EV-DV systems is how to increase throughput of data services without sacrificing voice service QoS requirements. The main motivation for our proposed architecture, MQC (multiple quality control), is to use per-stream buffers in the physical layer and to fully utilize air resources. The problem of existing architectures is that PDUs from only one stream can be transmitted and those PDUs may not fill out the allocated time slots. In contrast to existing architectures, MQC can multiplex several streams into one PLP (physical layer packet) because MQC utilizes per-service buffers in the physical layer. We also introduce a multiplexing scheme that minimizes queueing delays of real-time traffic and maximizes cell throughputs.

While traffic volume of real-time applications is rapidly increasing, current routers do not guarantee minimum QoS values of fairness and they drop packets in a random fashion. If routers provide a minimum QoS, resulting a less delay, reduced delay-jitter, more fairness, and smooth sending rates, TCP-friendly rate control (TFRC) can be adopted for real-time applications. We propose a dynamic buffer management scheme that meets the requirements described above, and can be applied to TCP flow and to data flow for transfer of real-time applications. The proposed scheme consists of a virtual threshold function, an accurate and stable per-flow rate estimation, and a per-flow exponential drop probability. We discuss how this scheme motivates real-time applications to adopt TCP-friendly rate control.