Gemini: A Green Deployment Scheme for Internet of ... - IEEE Xplore

Gemini: A Green Deployment Scheme for Internet of Things Yanbing Liu

Yu Meng

Jun Huang

School of Computer Science School of Computer Science School of Communication and and Technology and Technology Information Engineering Chongqing Univ. of Posts and Telecom. Chongqing Univ. of Posts and Telecom. Chongqing Univ. of Posts and Telecom. Chongqing, China 400065 Chongqing, China 400065 Chongqing, China 400065 Email: [email protected] Email: [email protected] Email: [email protected]

Abstract—The Internet of things (IoT) has been realized as one of the most promising networking paradigms that bridges the gap between the cyber and physical world. Developing green deployment scheme for IoT is a challenging issue since IoT achieves larger scale and reaches more complex so that the most of current schemes for deploying Wireless Sensor Networks (WSNs) cannot be reused. This paper addresses this challenging issue and proposes Gemini, a Green deployment scheme for internet of Things, that emphasizes modeling and optimization on green IoT deployment. The contributions made in this paper include: (i) A hierarchical system framework for general IoT deployment. (ii) An optimization model on the basis of proposed system framework to realize the IoT toward green. And (iii) a minimal energy consumption algorithm for solving the optimization model. Through numerical experiments, the results show that the Gemini proposed in this paper can work flexibly and energy-efficiently with both deterministic and random networking settings, thus is applicable to the green IoT deployment.

I. I NTRODUCTION The Internet of things (IoT) has been realized as one of the most promising networking paradigms that bridges the gap between the cyber and physical world. The prevalence of IoT leads toward a new digital context for configuring novel applications and services. IoT consists of a variety of things or objects such as Radio Frequency Identification (RFID) tags, sensors, actuators, mobile phones etc. which are interconnected through both wired and wireless networks to the Internet [1]. Objects in IoT can sense the environment, transfer the data, and communicate with each other, they become powerful tools to understand physical world and to response to emergent event and irregularities promptly. Thus, the IoT is seen by many as the ultimate solution for getting insights into physical processes in the real-world and in realtime. In parallel, the advancement of IoT brings some challenge for its implementation. Differing from traditional wireless sensor networks (WSNs), IoT achieves larger scale and becomes more complex. These turn out that schemes for deploying WSNs that can no longer be reused in the IoT. On the other hand, since IoT would connect more objects and consume higher power, green issues should also be taken into consideration while placing “things” in IoT. Green networking ____________________________________

plays a vital role in deploying IoT - they can reduce emissions and pollution, exploit environmental conservation and surveillance, and minimize operational costs and power consumption. Therefore, how to cost-effectively realize green deployment for IoT has become a crucial problem, which is the research focus of this paper. Recently researcher has made exciting progress on energy managing in WSNs. Their research perspectives can be classified into three major categories: energy saving in MAC layer [2], [3], [4], reducing energy by topology control [5], [6], [7], [8], [9], and lowering energy consumption via optimized scheduling [10], [11], [12], [13], [14]. However, none of these researches are concerning IoT deployment with green networking consideration. In this paper, we investigates how to cost-effectively arrange objects in the network to form a green IoT. To this end, we propose Gemini, a Green deployment scheme for internet of Things. Specifically, we first give a hierarchical system framework for IoT deployment. The framework capture the scale feature of IoT and thus enabling it extensible. Then, we present an optimization model on the basis of such framework, where the model is constrained in terms of energy consumption, link flow balance, and system budget, which realize the IoT toward green. Finally, we devise a minimal energy consumption algorithm, which leverages network routing principle to handle the optimization model. We show that Gemini can flexibly and energy-efficiently work with both deterministic and random network settings so that it is applicable to the IoT. The contributions of this paper are summarized as follows.

978-1-4673-5699-2 /13/$31.00 ©2013 IEEE

•

•

We present a hierarchical framework for placing network elements i.e., objects/things in IoT. Through such tiered framework, the scale feature of IoT can be captured so as to enable the IoT extensible. To the best of our knowledge, we are the first to present a general hierarchical framework for green IoT deployment. We model a green IoT by considering energy consumption, link flow balance, and system budget as an optimization problem based on the presented framework. We then propose a minimal energy consumption algorithm, which leverages network routing principle, to solve

Convergence layer

Relay layer

Base Station Relay Node Object/Thing

Sensing layer

Wireless Link Wireless Link

Fig. 1.

An example of system framework for IoT deployment.

such optimization problem. We believe that our proposed algorithm makes the deployed IoT achieving a green one. • We conduct extensively numeric experiments on both deterministic and random networked IoT. Our results show that the proposed model and algorithm are flexible and energy-efficient for IoT deployment. The remainder of this paper is organized as follows. Section II describes the system framework for placing network elements in IoT. Section III formulates the problem of green IoT deployment and formally presents the optimization model. A minimal energy consumption algorithm in solving such optimization problem is also proposed in this section. Section IV presents the experimental results to validate both the proposed model and algorithm. Section V concludes this paper. II. S YSTEM FRAMEWORK In order to make the IoT scalable, we propose a tiered framework for IoT deployment upon our previous practical engineering experiences in deploying large-scale WSNs. We found in WSNs that, dynamic routing mechanisms are almost not applicable outdoors because factors like electromagnetic interferences, air humidity, as well as temperature etc. have great impact on its data transmission. More importantly, WSNs with dynamic routing protocol configured cannot achieve larger scale. Inspired by these realities, we thus argue that equipments in IoT should be placed in a hierarchical structure and configured by static routing mechanisms, which is also the main philosophy taken in our proposed tiered framework. Fig. 1 shows a paradigmatic example of system framework for IoT deployment that includes three layers, i.e., Sensing layer, Relay layer and Convergence layer from bottom to up. Sensing layer is used for placing objects and things (e.g. RFID etc.), Relay layer is formed by a collection of relay nodes, and Convergence layer consists of several base stations who further connect to the Internet. With the purpose of energy saving and link load balancing, the objects/things (sensing nodes) in the sensing layer are not allowed to communicate with each other directly. Instead, the communication between any two objects is working through the relay node. That is, equipments in sensing layer can only send/receive data to/from the relay node in the upper layer. While for the relay nodes in the Relay layer, they form a relay network where any two nodes are

reachable. The other major functionality of relay node is to relay the data from sensing layer to the base station in the upper layer. As for the convergence layer, base stations in this layer are also connected to be a network, which further uploads the data to the Internet. By placing IoT in above hierarchical fashion, the proposed system framework provides flexibility, promotes scalability and promises increased manageability. One of the major benefits introduced by such tiered paradigm is that equipments in IoT do not need to be installed by sophisticated chips and run complex routing mechanisms, and thereby a large amount of budget could be saved. In order to enable the tier-deployed IoT to be green, we first formally formulate the system framework in the following. Let x and y be the two points in Euclidean plane, d(x, y) be the distance between x and y, denote S as the set of l sensing nodes (objects or things) in the sensing layer, r > 0 as the communication radius of each node. Also, denote R as the set of m relay nodes, R ≥ r as the communication radius of each relay node. Let B be the set of n base stations, and assume the communication radius of a base station is fairly large. Denote the entire network of IoT is G(N, A) where N represents the node set and A represents the wireless link set, then the communication policy of any two nodes in IoT can be outlined below. 1) To any i ∈ S, j ∈ S, i and j cannot communicate with each other even d(i, j) ≤ r, 2) To any i ∈ S, j ∈ R, if d(i, j) ≤ r, i can send data to j, 3) To any i ∈ R, j ∈ R ∪ B, if d(i, j) ≤ R, i and j can reach each other. With these notations and symbols in hand, we herein make the following assumptions for the system framework. • All the nodes in the framework are fixed and in the fixed sites. • Nodes in the same type has the same attribute, e.g. initial energy, energy consumption parameters, maximum sending power, minimal receiving power, and so forth. • Nodes in the Sensing layer can send data to a base station in a multi-hope manner. • Each node in both Sensing and Relay layer is energyconstrained, while base station is not. • The whole network of IoT G(N, A) represents a connected network, that is, each node in the Sensing layer has a path to a base station, so does the relay node. In the next section, we will model the IoT with “green” requirements based on above assumption of system framework. III. M ODELING THE G REEN I OT In this section we start with the variable and notations definition used throughout the paper, then we formulate the system constraints according to the “green” requirements on IoT. Next, we address the IoT green deployment as an optimization problem. Finally, we propose an algorithm to solve such problem. We also discuss the performance of our proposed algorithm in this section.

A. Variable Definition Listed following are notations of variables and parameters used throughout this paper. Etx , Erx : the energy consumption of node transmits data and receives data, respectively. Eelec : the energy consumption of radio electronics. 0 , 1 , 2 : node parameter, sensing node parameter, and relay node parameter respectively. dij : the distance between node i and node j. L: the data length. Fij : the data rate of node i sending information to j. Hmax : maximum data rate of a link. CS , CR , CB : the monetary cost of sensing node, relay node, and base station. |·|: the cardinality of a set. l, m, n: the cardinality of set S, R, B W0 : the system budget. B. System Constraints As we aforementioned, in the tiered system framework a sensing node can only communicate with the relay node in the upper layer, whereas the relay node can send/receive data both to/from its neighbor relay node as well as the base station . Hence, G(N, A) is a directed and connected graph. We call node i and node j neighbors if i and j is able to communicate with each other. Let N (i) be the set of i’s neighbors, C be the adjacent matrix of G(N, A), then ⎤ ⎡ c11 c12 · · · c1|N | ⎢ c21 c22 · · · c2|N | ⎥ ⎥ ⎢ (1) C=⎢ . ⎥ .. .. .. ⎦ ⎣ .. . . . c|N |1 c|N |2 · · · c|N ||N | where cij = 1 if j ∈ N (i), otherwise cij = 0. To address the green requirements, we consider the following system constraints. 1) Energy Consumption Constraints: Since the energy consumption of data communication is larger than that of data sensing and processing, only the energy consumption of data communication is taken into account in the model. That is, the energy of a node sending and receiving data. According to the Friis free space model [15], we have Etx = (Eelec + 0 · d2 ) · L

(2)

Erx = Eelec · L

(3)

Upon above two equations, the energy consumption of each node in a time unit can be calculated by ei =

cij · Fij · (Eelec + 1 · d2ij )

∀i ∈ S

(4)

ek =

+

i∈S∪R i∈B∪R

∀k ∈ B

cij · Fij + cji · Fji ≤ Hmax

(5)

(7)

where ∀i, j ∈ R Likewise, for the sensing node and base station, the wireless links need to meet the following constraint cij · Fij ≤ Hmax

(8)

where ∀i ∈ S, j ∈ R or ∀i ∈ R, j ∈ B It is worthwhile to notice that, by placing a Relay layer over the Sensing layer in IoT, the relay nodes carry the most of network loads. Since the performance of the relay node is relatively strong, one of the outcomes in layering relay nodes in our proposed framework is that the link flow can be balanced. Compared with the Ad hoc scheme, though the node is capable of transmitting data to its neighbors, it seems that the node near to the sink or base station is more likely dead due to the overload. Therefore, we advocate that the tiered framework is more preferable for IoT deployment, and it is fully able to balance the loads, thus prolonging the network lifetime. 3) System Budget Constraint: Since the cost of the relay node and base station are comparatively expensive, the deployment of an IoT must as cheaper as possible. Consequently, the IoT should meet the system budget constraint, i.e. 0 < CS · l + CR · m + CB · n < W0

∀j ∈ R

(6)

where ei , ej , and ek denotes the consumption of sensing node, relay node, and base station respectively. Note in Eq. (4) that, we only consider the case that the sensing nodes send data to the upper layer, i.e., the energy of a sensing node for receiving data is ignored. This is due to the fact that the data received by a sensing node are usually the signalling messages, the size of which is much smaller than that of sensing data. Therefore, the energy consumption of receiving data in a sensing node could be omitted. Similarly, Eq. (5) excludes the energy of receiving data from the base station and that of transmitting data to the sensing node. Also, Eq. (6) omits the energy consumption when a base station sends data. 2) Link Flow Balancing Constraints: In the IoT, the bandwidth of a node is constrained except for the base station. For a relay node, it communicates not only its neighbor relay nodes, but also the sensing node in the lower layer. Thus, the wireless links of a relay node should satisfy

cij · Fij · Eelec cji · Fji · (Eelec + 2 · d2ji )

cjk · Fjk · Eelec

j∈R

j∈R

ej =

(9)

With above system constraints, we are now ready to present the optimization model for green IoT deployment.

Algorithm 1 MECA Input: S, X of candidate relay node locations, B, R ≥ r > 0 Output: Minimal Energy Consumption min(e) 1: for i ∈ S, j ∈ X do 2: Calculate the distance dij between i and j; 3: if dij < r then 4: Add the node j to a candidate set RN for placement, add i to N (j); 5: end if 6: end for 7: Select q > 0 sets from all N (j) and unify them to form a union set; 8: if G(RN ∪ B, A) is a connected graph and ∪N (j) == S then 9: Set the energy consumption in terms of (4), (5) and (6) as the weight on each edge; 10: Apply Dijkstra to calculate a shortest path p from i ∈ S to k ∈ B in the graph G(S∪RN ∪B, A), sum the length of all the shortest path, denoted as L; 11: end if 12: Find a minimal L among all L, denoted as min(L); 13: Set min(e) = min(L); 14: return min(e);

C. An Optimization Model for Green IoT Deployment The main purpose of this paper is to reduce the energy consumption to achieve the green IoT. Hence, the optimization model for green IoT deployment is defined as

min

i∈S

ei +

j∈R

ej +

k∈B

j∈R

i∈S

j∈R

k∈B

Now we show that the worst-case time complexity of MECA is the same with that of Dijkstra by the following theorem. Theorem

1: The worst-case time complexity of MECA is 2 O |N | Proof: The first for-loop from line 1 to line 6 in MECA takes O(l · m) eliminate the locations for placing relay nodes. Line 7 spends a constant time to generate a union set. The 2 if-statement in line 8 takes O (m + n) to examine the connectivity of the graph. Line 9 consumes O(1) time, and

2 line 10 spends O (l + m + n) because the adjacent matrix is used in this paper for representing the graph. Line 12, 13, and 14 are all taking the constant time. Therefore, the worstcase time complexity of MECA is

2 2 O l · m + (m + n) + (l + m + n)

2 = O |N | where |N | = l + m + n

IV. N UMERIC E XPERIMENTS

ek

s.t. ei = cij · Fij · (Eelec + 1 · d2ij ) j∈R cij · Fij · Eelec ej = i∈S∪R + cji · Fji · (Eelec + 2 · d2ji ) i∈B∪R cjk · Fjk · Eelec ek =

In the first step (from line 1 to line 6), MECA removes the locations for relay node who are out of the communication range of sensing nodes. In this way, the search space of optimization problem is dramatically reduced. The second step of MECA, i.e., line 7 detects an available tiered deployment scheme for the IoT. In the third step of MECA, that is line 8 and 14, it maps the energy of node to a weight on each edge, then the Dijkstra is applied to find the solution min(e) for optimization problem (10), where ⎤ ⎡ ei + ej + ek ⎦ min(e) = min ⎣

A. Experiment Setup ∀i ∈ S

∀j ∈ R

(10)

∀k ∈ B

∀i, j ∈ R cij · Fij + cji · Fji ≤ Hmax cij · Fij ≤ Hmax ∀i ∈ S, j ∈ R or ∀i ∈ R, j ∈ B 0 < CS · l + CR · m + CB · n < W0 D. A Minimal Energy Consumption Algorithm To solve the problem derived from above optimization model, we devise an Minimal Energy Consumption Algorithm (MECA) as shown in Algorithm 1. The basic idea behind MECA is to first confine the placement of relay nodes [16], [17], then leverage network routing principle to transform the original problem as a routing problem, and eventually resolve it by a well-known shortest path algorithm, say Dijkstra. Concretely, MECA works in the following three steps.

In this section, we validate the Gemini through numeric experiments. Both of the deterministic and random topology are used here to test the minimal energy consumption of the IoT. The nodes in both topologies are distributed in a 50×60m2 region. Fig. 2 shows a deterministic topology where l = 15, the number of candidate locations for placing relay node is 36, and m = 1, while Fig. 3 depicts a random topology. The detailed topology settings (as well as the source code) used in the experiments can be found in [18]. The parameters are configured as follows. We set Eelec = 50nJ/bit, 1 = 2 = 100pJ/bit/m2 , Fij = 100kbps for sensing node, Fij = 200kbps for relay node, and Hmax = 400kbps. We examine the variation of IoT’s minimal energy consumption when the parameters such as communication radius, number of base stations, and number of sensing nodes varies. B. Experimental Results Fig. 4 shows the minimal energy consumption of the IoT versus number of relay nodes in the deterministic topology. Fig. 4(a) plots the curves with the communication radius

60

Sensing node Relay locations Base station

4(b) it can be seen that the more base stations are, the smaller energy consumption of system is. This is intuitive that more base stations could balance the network load potentially. As the curves in Fig. 4(c), they also give an natural trend, that is, with the increasing of number of sensor nodes, the minimal energy consumption of the system increases. Fig. 5 shows the minimal energy consumption of the IoT versus number of relay nodes in the random topology. As expected, it follows the same pattern with the Fig. 4. To sum up, the proposed algorithm MECA is effective for solving the optimization problem.

50

Y

40

30

20

10

0 0

10

20

30

40

50

X

Fig. 2.

C. Discussion

A deterministic topology used in the experiment. Sensing node Relay locations Base station

60 50

Y

40 30 20 10 0 0

10

20

30

40

50

X

Fig. 3.

A random topology used in the experiment.

changing when l and m was set to be 15 and 1, respectively. Fig. 4(b) depicts the data with the number of base station varying if l = 15, R = 2r = 30m. And Fig. 4(c) presents the results with the variation of number of sensing nodes if n = 1, R = 2r = 30m. From these three sub-figures we can observe that, the minimal energy consumption of the IoT decreases and tends to be steady when the number of relay nodes increases. This result follows our earlier analysis on the proposed algorithm that MECA is able to obtain a smallest number of relay nodes while minimizing the entire system’s energy consumption. In particular, it can be observed from Fig. 4(a) that when r = 15, R = 15 or R = 30, and m < 5, the relay nodes do not cover all the sensing nodes so that the IoT network is unconnected, which in turn leads the minimal energy consumption to be zero. Note that we assume the minimal energy consumption here is zero in the case that the graph is unconnected. In addition, we find if the number of relay nodes is fixed while the communication range of the sensing node becomes smaller, the candidate locations who are closer to the sensing node are more likely selected for relay nodes placement in order to maintain the connectivity of the IoT network. In this regard, the relay nodes would be placed far away from the base station that may consume more energy. Thus, we can also seen that for the case of R = r = 20m, it takes the smallest energy consumption among three cases. Similarly, Fig. 4(b) and Fig. 4(c) show the same insights when the number of relay nodes less or equal to 5. In Fig.

It is challenging to establish a baseline to compare with our proposed scheme, i.e. Gemini, since existing researches do not specialized in the IoT deployment with green networking consideration and any power-saving schemes we pick from WSNs might result in the risk of an “unfair game”. For this reason, we only implemented the Ad hoc schema in above two topologies as the baseline for comparison study. The communication radius of sensing node was set to be 15 and 20, respectively. We obtained the total energy consumption of the system. For the case of l = 15, l = 30, l = 50, the energy of the system is 0.48428J, 0.98096J, and 1.65157J which are smaller than that of Gemini. While in the random topology, we can get the energy of the system under the Ad hoc manner is 0.3482J, 0.62944J, and 1.04517J that are follow the identical outcome. Though the energy consumption of this scheme is much smaller than that of Gemini, the nodes, as we stated earlier, who near to the base station are more likely dead because they would be overloaded. Therefore, we believe that the proposed Gemini can work energy-efficiently and balancing the network load, thus are applicable to green IoT deployment. V. C ONCLUSION The prevalence of IoT lower the barrier from real world to Internet which leads toward a new digital context for configuring novel applications and Services. Establishing green schema for IoT plays a vital role in IoT massive deployment. In this paper, we investigated how to cost-effectively arrange objects in the network to form a green IoT. We proposed Gemini, a Green deployment scheme for internet of Things. Specifically, we first gave a hierarchical system framework for IoT deployment. The framework can capture the scale feature of IoT and thus enabling it scalable. Then, we presented an optimization model on the basis of such framework. The model is constrained in terms of energy consumption, link flow balance, and system budget, which realize the IoT toward green. Finally, we devised a minimal energy consumption algorithm leveraging network routing principle to handle the optimization model. Through experiments, we showed that Gemini can flexibly and energy-efficiently work with both deterministic and random networking environments so that it is applicable to the real-world IoT green deployment.

1.4 1.3 1.2 1.1 1.0 0.9

4.5

n=1 n=2 n=3

0.85 0.80

Minimal energy consumption (J)

R=2r=30m R=r=20m R=r=15m

1.5



1.6

0.75 0.70 0.65 0.60 0.55 0.50

2

3

4

5

6

7

8

9

10

1

2

3

Number of relay nodes

4

5

3.0 2.5 2.0 1.5

6

7

8

9

1

10

2

3

4

(b) Fig. 4.

7

8

9

10

8

9

10

(c)

1.18

0.70

0.65

0.60

1.16


0.75

2.7

n=1 n=2 n=3

1.17


0.80

6

Minimal energy consumption of IoT system with deterministic network setting.

R=2r=30m R=r=15m R=r=20m

0.85

5



(a)


3.5

1.0

0.45

1

l=15 l=30 l=50

4.0

1.15 1.14 1.13 1.12 1.11 1.10 1.09

l=15 l=30 l=50

2.4 2.1 1.8 1.5 1.2 0.9

1.08

0.6 1

2

3

4

5

6

7

8

9

10


1

2

3

4

5

6

7

8

9

10

1

2


(a)

(b) Fig. 5.

3

4

5

6

7


(c)

Minimal energy consumption of IoT system with random network setting.

ACKNOWLEDGMENT This work is supported by CQUPT Research Fund for Young Scholars (Grant No. A2012-79), and partially supported by NCET, National Science Foundation of China (Grant No. 61272400), Chongqing Municipal Education Commission (Grant No. KJ120512) R EFERENCES [1] L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: a Survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, Oct. 2010. [2] A.B. Nacef, S.M. Senouci, Y.G. Doudane, and A.L. Beylot, “ECAR: an Energy/Channel Aware Routing Protocol For Cooperative Wireless Sensor Networks,”in Proc. PIMRC, Toronto, Canada, pp. 964–969, 2011. [3] I. Amadou, G. Chelius, and F. Valois, “Energy-Efficient Beacon-less Protocol for WSN,”in Proc. PIMRC, Toronto, Canada, pp. 990–994, 2011. [4] O. Soysal, S. Ayyorgun, and M. Demirbas, “PowerNap: An Energy Efficient MAC Layer for Random Routing in Wireless Sensor Networks,” in Proc. SECON, Utah, USA, PP. 10–18, 2011. [5] X. Wang, X. Wang, and J. Zhao, “Impact of Mobility and Heterogeneity on Coverage and Energy Consumption in Wireless Sensor Networks,” in Proc. IEEE ICDCS , Minneapolis, USA, PP. 477–487, 2011. [6] X. Liu, J. Cao, S. Lai, C. Yang, H. Wu, and Y. Xu, “Energy Efficient Clustering for WSN-based Structural Health Monitoring,” in Proc. INFOCOM, Shanghai, China, pp. 2768–2776, 2011. [7] K. Han, L. Xiang, J. Luo, and Y. Liu, “Minimum-Energy Connected Coverage in Wireless Sensor Networks with Omni-Directional and Directional Features,” in Proc. MobiHoc, Hilton Head Island, USA, pp. 85–94, 2012. [8] M. Aslam, T. Shah, N. Javaid, A. Rahman, Z. Rahman, and Z.A. Khan, “CEEC: Centralized Energy Efficient Clustering A New Routing Protocol for WSNs,”in Proc. SECON, New Orleans, USA, pp. 103–105, 2012.

[9] P.M. Wightman and M.A. Labrador, “A3Cov: a new topology construction protocol for connected area coverage in WSN,” in Proc. IEEE WCNC, Cancun, Mexico, pp. 522–527, 2011. [10] G. Ghidini and S. K. Das, “An Energy-efficient Markov Chain-based Randomized Duty Cycling Scheme for Wireless Sensor Networks,” in Proc. ICDCS, Minneapolis, USA, pp. 67–76, 2011. [11] Y.X. Zhao, J. Wu, F. Li, and S. Lu, ”VBS: Maximum Lifetime Sleep Scheduling for Wireless Sensor Networks Using Virtual Backbones,” in Proc. IEEE INFOCOM, San Diego, USA, pp. 1-5,2010. [12] J. Feng, C.W. Chang, S. Sayilir, Y.H. Lu, B. Jung, D. Peroulis, and Y.C. Hu, “Energy-Efficient Transmission for Beamforming in Wireless Sensor Networks,” in Proc. IEEE SECON, Boston, USA, 2010. [13] T.L. Porta, C. Petrioli and D. Spenza, “Sensor-mission Assignment in Wireless Sensor Networks with Energy Harvesting,” in Proc. IEEE SECON, New Orleans, USA, pp. 413–421, 2011. [14] K. Oikonomou and S. Aissa, “Dynamic Sink Assignment for Efficient Energy Consumption in Wireless Sensor Networks,” in Proc. IEEE WCNC, Paris, France, pp. 1876–1881, 2012. [15] W. Heinzalmen, A. Chandrakasan, and H. Balakrishman, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. on Wireless Commun. vol. 1, no. 4, pp. 660–670, 2002. [16] S. Misra, S. Hong, G. Xue, and J. Tang, “Constrained relay node placement in wireless sensor networks: formulation and approximations,” IEEE/ACM Trans. on Networking, vol. 18, no. 2, pp. 443–447, 2010. [17] D. Yang, S. Misra, X. Fang, G. Xue, and J. zhang, “Two-tiered constrained relay node placement in wireless sensor networks: computational complexity and efficient approximations,” IEEE Trans. Mobile Comput., vol. 11, no. 8, pp. 1399–1411, 2012. [18] https://code.google.com/p/my-project-gemini/downloads/list