参与式传感系统中的节能数据传输与共享策略
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"参与式传感系统中具有数据共享的节能传输"
在当今信息化时代，参与式传感系统（Participatory Sensing Systems）已经成为物联网(IoT)领域的一个重要研究方向。这种系统允许智能手机用户通过执行任务来收集和共享数据，为公众提供实时信息。然而，随着系统规模的扩大和用户数量的增加，数据传输带来的能源消耗问题变得日益显著。这篇被接受发表的研究论文探讨了如何在参与式传感系统中实现节能的数据传输，并利用数据共享来降低能耗。
论文作者包括Weiwei Wu、Jianping Wang、Minming Li、Kai Liu、Feng Shan以及Junzhou Luo，他们都是电气电子工程师学会(IEEE)的会员或资深会员。文章指出，当多个任务请求来自同一移动用户的同一类型数据时，用户可以通过智能地制定传输计划，以平衡传输的数据量和能量消耗，从而实现能源效率的提升。
在传统的数据传输模式中，每个任务请求都会导致用户发送完整的数据包，这无疑加剧了电池的损耗。而论文提出的数据共享策略则试图通过减少重复数据传输来优化能源使用。具体来说，当一个用户已经为一个任务传输了特定数据后，如果其他任务也要求相同的数据，那么就可以复用这些数据，避免了多次传输同一信息，从而节省能量。
为了实现这一目标，论文可能涉及以下几个关键技术点：
1. 数据压缩与编码：通过高效的数据压缩技术减少需要传输的数据量，同时利用编码技术确保数据的完整性和可复用性。
2. 联合调度算法：设计智能的传输调度算法，考虑任务优先级、数据相似度和设备电量等因素，以确定最优的数据传输策略。
3. 本地数据存储与缓存：利用设备的本地存储空间，将已传输的数据暂时保存，以便于后续任务的复用，减少不必要的网络交互。
4. 无线通信协议优化：针对参与式传感系统的特性，可能需要调整或定制无线通信协议，以支持更节能的数据传输。
5. 安全与隐私保护：在数据共享的同时，必须确保用户数据的安全性和个人隐私，可能需要采用加密技术或其他安全措施。
通过这些方法，参与式传感系统可以实现更加绿色和可持续的发展，同时满足公众对实时数据的需求。论文的最终版本可能会进一步深入讨论这些技术的实现细节、性能评估和实际应用案例，以验证其在实际环境中的有效性。
0733-8716 (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSAC.2016.2621359, IEEE Journal
on Selected Areas in Communications
3
on the total number T of time slots that is usually large,
and moreover, we further investigate the generalized AD task
model by developing an O(ln
2
L)-competitive algorithm in
Section VI. Section VII performs the simulations for our
online algorithms and validates their efficiency. Finally, we
conclude the paper in Section VIII.
II. RELATED WORK
Extensive research work has been done respectively in the
field of participatory sensing and rate scheduling. We only
review the most related ones due to space limit.
Participatory sensing: Recently, with its attractive appli-
cations, participatory sensing has attracted extensive research
attention, both from industry and academia. Various issues
in participatory sensing have been addressed in the literature
[1], [2], [12], [13]. For example, [14], [15], [16] use social-
media-based crowdsourcing to build knowledge base of urban
emergency events; [18], [19] consider the privacy preserving
problem in participatory sensing; [20], [21] study the incentive
mechanism design problem in incentivizing truthfulness and
users’ participation; [17] considers location-aware collabora-
tive sensing in mobile crowdsourcing. The task allocation
issues in crowdsourcing markets are investigated in [22], [23],
without concerning about data sharing, however. The data shar-
ing problem within multiple applications is initially formulated
in the field of wireless sensing systems, in which Tavakoli et
al. [3] and Fang et al. [4] consider constant-rate schedules and
develop algorithms to minimize the communication overhead.
Observing the data sharing nature, Zhao et al. [24] extend
their work to study the sensing task allocation problem in
participatory sensing system with the aim of load balancing
over multiple participants.
Energy-efficient rate-adaptive scheduling: Much research
effort has been made to design rate-adaptive transmission
algorithms (without considering data sharing). Various ob-
jectives (e.g., throughput, delay, energy consumption) are
investigated in prior works which can be referred to in a
recent survey [25]. [26], [9], [27] devise the optimal rate
schedule to minimize the energy consumption in the offline
setting, respectively with the input of common deadline tasks
and FIFO tasks. Gatzianas et al. [28] investigate the system
utility maximization problem from a stochastic aspect. [29],
[30], [31], [32] are the first works to theoretically study the
algorithms in the general online setup, without relying on
any prior information. Among them, Vaze et al. [29] develop
online algorithms to minimize the completion time; [30],
[31], [32] propose online algorithms with competitive ratios
to maximize the data throughput. In the literature, no online
rate scheduling algorithms with competitive ratios are known
for minimizing the energy consumption.
To the best of our knowledge, no prior works considered
the rate-adaptive transmission policies with data sharing in
participatory sensing systems. This paper addresses such a
problem and we notice that there is a surprising conflict
between the data traffic and energy consumption in such a
rate-adaptive scenario, which is in contrast to the constant-rate
scenario [4], [24] where data traffic minimization is consistent
TABLE I
KEY NO TATIONS
Symbol Semantics
J input task set
J
i
i
th
task
r
i
arrival time of task J
i
d
i
deadline of task J
i
w
i
amount of data requested by task J
i
L the longest length of the time duration among all tasks in J
T the latest deadline of the tasks in J
p = G(s) rate-power function, the power consumed to achieve the rate s
s(t) data rate specified in time t
W (A) data traffic caused by schedule A
E(A) energy consumption caused by schedule A
with the energy minimization, thus we attempt to balance such
a trade-off by developing online algorithms with performance
bounds in terms of bi-objectives.
III. PRELIMINARIES
In this section, we first introduce the system model, and
then present the problem formulation.
A. System model
We model a QoS-constrained request in a participatory
sensing system as a task with an arrival time and a deadline,
specifying the timeliness of the data request. The time is
partitioned into discrete time slots, 1, 2, ..., T, and we use time
interval [t
1
, t
2
] to refer to time slots t
1
, t
1
+ 1, . . . , t
2
. Thus
the length of the interval is t
2
− t
1
+ 1 time slots. A task
J
i
, i = 1, . . . , n, can be defined by (r
i
, d
i
, w
i
), where r
i
is
the arrival time, d
i
is the deadline, and w
i
is the amount of
data requested. To be specific, task J
i
can receive data from
the beginning of time slot r
i
to the end of time slot d
i
. For
simplification, we just say task J
i
arrives at r
i
and departures
at d
i
. The amount of transmitted data in interval [r
i
, d
i
] should
be at least w
i
, which is called the delay/time constraint.
All tasks form the input task set J = {J
1
, J
2
, ..., J
n
}. Let
L = max
1≤i≤n
d
i
− r
i
+ 1 be the longest length of the time
duration of the tasks.
Without loss of generality, we assume min
i
r
i
= 1 and
max
i
d
i
= T . For each task J
i
= (r
i
, d
i
, w
i
), we say that
task J
i
remains alive at t ∈ [r
i
, d
i
]. Also, r
i
(d
i
) is called an
arrival (deadline) time/point, and consequently, from time 1
to T , there are 2n such points. The time interval between two
adjacent points is called a block/epoch.
In this paper, we consider two task models, FIFO task model
and AD task model. The FIFO models the first-in-first-out
service rule where tasks have deadlines in the same order as
the arrival times, which is the most general task model studied
in the literature of rate scheduling [8], [9]. The AD task model
further generalizes FIFO to tasks with Arbitrary Deadlines to
model the most general QoS requirement.
We consider a single user point-to-point transmission chan-
nel and make the same assumption as previous works that
the transmitter can adaptively change its transmission rate s,
which is related to transmission power p through a function
p = G(s) called rate-power function. In many systems with
realistic encoding/decoding schemes, the rate-power function
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